AIYING

Lateral AI #001 — What If AI Couldn’t Answer? From Recommendations to Learning Discovery

2026-11-02T21:43:12.879+05:30

AIYING • LATERAL AI #001

What If AI Couldn’t Answer?

Change the thinking. Discover the possibility.

When AI gives you more options, do you always gain more clarity? This first Lateral AI experiment explores what happens when we temporarily remove the answer.

Watch the Short

Watch on YouTube

1. More recommendations, less clarity?

Imagine you are choosing a course. You ask AI, “Which course should I take?” It produces an impressive list. You compare subjects, certificates, formats, and fees. Yet you still cannot decide.

The hidden assumption is that you already understand your learning need well enough to request a recommendation. Perhaps the missing step is discovering what you actually want to become capable of doing.

This is an illustrative scenario, not a reported case study.

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2. The thinking concept: PO

In PO: Beyond Yes & No, Edward de Bono presents PO as a tool for moving beyond established patterns. A provocative statement becomes a starting point for exploration: we temporarily suspend immediate judgment and look for where it can lead.

For this AIYING adaptation, our provocation is:

PO: AI cannot recommend a course.

We use this temporary restriction to explore another role for AI. After generating possibilities, we return to judgment and test their usefulness.

Source and adaptation: PO is de Bono’s concept. The course-selection scenario, prompt, and experiment below are contemporary AIYING applications.

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3. Make the thinking movement visible

Remove recommendations temporarily. What useful assistance remains?
Extract a principle. AI can help clarify the need behind the request.
Explore the difference. Instead of comparing courses, investigate the task you want to perform.
Develop a possibility. Design a small trial task that reveals what you need to learn.

Suppose your goal is to create a simple personal website. Before enrolling, you attempt a basic page. You discover whether your main difficulty is writing, visual design, or building the page. That evidence can make your next learning choice more specific.

The discovery: A useful first output could be a learning experiment rather than a course list.

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4. Copy and try this prompt

I am considering taking a course, but first help me clarify my learning need.

For now, do not recommend courses.

Ask me one question at a time about:
- What I want to become capable of doing.
- A real situation where I would use that capability.
- What I have already tried.
- My available time, resources, and constraints.

After no more than five questions, summarize my goal and any assumptions that remain untested. Ask me to correct your summary.

Then help me design one small task I can attempt in 20 minutes using resources I already have. Define an observable result and what to notice when I get stuck.

After I report the result, help me identify my learning gaps. Distinguish evidence from guesses. We can then consider whether a course is appropriate.

Adapt the time limit to your situation. If you later compare actual courses, verify their current syllabus, fees, availability, and entry requirements directly.

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5. Run a small experiment

Use the same learning goal in two separate AI conversations.

Conversation A: Ask directly for course recommendations.
Conversation B: Use the question-first prompt above and attempt the task.

Compare the results: Can you state a specific capability you need? Can you name a gap supported by your task attempt? Do you have a feasible next action? Record a concrete example for each answer.

Notice what each approach contributes. This personal experiment can inform your next step; it does not establish that one method is universally better.

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6. Reflect and apply

What assumption did you discover in your original question?
How did removing recommendations change the conversation?
What did the task reveal that an AI answer alone could not?

Try the same movement when choosing a tool or planning a personal project: temporarily remove recommendations, clarify the purpose, and design a small test.

Share your discovery: What happened when you asked AI to help you find the question before providing the answer?

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AI Creativity #002 — What If AI Stopped Agreeing With You? | Edward de Bono's Challenge Technique

2026-11-02T20:16:53.869+05:30

AIYING | AI Creativity | Episode #002

What If AI Stopped Agreeing With You?

From Agreeable Assistant to Constructive Challenger

Break the Pattern. Discover a Direction. Develop with AI. Accomplish Something New.

Applying Edward de Bono's Challenge Technique to the age of generative AI.

1. The Reality: AI That Agrees With Us

Imagine asking an AI assistant:

"I want to change jobs because the new position offers a higher salary. Help me prepare for the interview."

The assistant enthusiastically provides interview questions, sample answers, and preparation tips.

That is useful.

But what if the person is overlooking something important?

Perhaps the new position offers fewer learning opportunities, poorer working conditions, or a less promising career path.

A conventional response may complete the requested task without examining the assumption behind it.

Hidden assumption: A helpful AI assistant should accept the user's initial framing and proceed with the requested task.

Is that always the most helpful approach?

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2. The Method: Edward de Bono's Challenge Technique

Edward de Bono's Serious Creativity (1992) describes deliberate methods for challenging established ways of thinking.

The creative challenge is not simply an attempt to prove something wrong.

Instead, it asks whether an existing approach must be followed in its familiar form.

We may ask:

Why is this done this way?
Is this arrangement necessary?
What purpose does it serve?
Could that purpose be achieved differently?
What alternatives become possible?

The objective is to open new directions for exploration.

CHALLENGE: Why must an AI assistant always accept the user's initial direction?

Notice that this is a challenge to an established assumption, not a claim that AI must always disagree.

Unlike the escape provocation explored in Episode #001, this episode begins by questioning the necessity of a familiar practice.

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3. Disrupt: Challenge the Assumption

Let us examine the conventional expectation of AI helpfulness.

What is its underlying purpose?

Presumably, it is to help people accomplish their goals.

But does accomplishing a goal always mean immediately following the first request?

Consider several alternatives:

AI identifies a hidden assumption before preparing a plan.
AI asks one constructive question before recommending a decision.
AI suggests an alternative framing of the problem.
AI highlights an overlooked trade-off.
AI helps the user examine the consequences of different choices.

The Deliberate Movement

We move from the original concept of helpfulness:

Conventional: Helpfulness means responding effectively to the request.

Toward a broader concept:

New direction: Helpfulness can also mean improving the thinking behind the request.

What if AI helped us discover what we were overlooking before helping us act?

This movement suggests a new AI interaction design.

The AI Constructive Challenger

An AI assistant that identifies important assumptions, explores alternative perspectives, and then helps the user accomplish the original goal.

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4. Explore: Build the Constructive Challenger

Now we use generative AI to develop the direction discovered through creative thinking.

The process is:

Identify a familiar assumption.
Challenge its necessity.
Explore an alternative arrangement.
Develop that alternative using AI.
Evaluate and test the result.

Reusable AI Prompt

AI CONSTRUCTIVE CHALLENGER

Act as my AI Constructive Challenger.

When I present a plan, decision, or request:

Identify one important assumption I may be overlooking.
Explain why that assumption deserves examination.
Offer one alternative framing of the situation.
Ask one constructive question that could improve my thinking.
After my response, help me accomplish my original goal.

Do not disagree merely for the sake of disagreement.

Distinguish evidence from speculation.

Respect my choice to proceed directly.

Begin by asking what decision or task I am working on.

Example: A Career Decision

User: I have received a job offer with a 25% salary increase. Help me accept it.

Conventional AI: Here is a professional acceptance email.

Constructive Challenger: Before drafting the email, may I challenge one assumption? Are you treating the salary increase as sufficient evidence that this is a better career move?

User: Perhaps. I have not compared the learning opportunities.

AI: Would it help to compare compensation, role scope, growth opportunities, working conditions, and long-term goals before you decide?

User: Yes. Let us compare them first.

The discovery: AI has not rejected the user's objective. It has helped the user examine an assumption that may affect the decision.

The example is illustrative. The actual value of this approach depends on context, user preferences, and the quality of the challenge.

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5. Judge: Is Constructive Challenge Valuable?

A creative direction becomes useful only when its value is examined.

Potential Benefits

Revealing hidden assumptions.
Identifying overlooked trade-offs.
Encouraging reflective decisions.
Exploring alternative possibilities.
Reducing uncritical acceptance of initial plans.

Potential Risks and Limitations

Excessive questioning may waste time.
Unnecessary disagreement may frustrate users.
AI-generated challenges may be based on incorrect assumptions.
Some requests require immediate execution.
Important decisions may require qualified human expertise.

A More Practical Design

Three AI Interaction Modes

Direct Mode: Complete the requested task.

Challenge Mode: Examine one important assumption before proceeding.

Explore Mode: Generate and evaluate alternative directions.

The user remains in control of which mode to use.

Constructive challenge should improve decisions, not become an obstacle to getting things done.

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6. Accomplish: Run a Real-World Experiment

We can investigate the value of constructive AI challenge through a simple experiment.

The Experiment

Select several everyday planning or decision tasks.
Use a conventional AI assistant for one set of tasks.
Use Constructive Challenger Mode for comparable tasks.
Record the assumptions identified by the AI.
Evaluate whether the challenges were relevant and useful.
Compare the resulting plans or decisions.

Suggested Evaluation Criteria

Measure	Question
Relevance	Was the challenge related to the real problem?
Discovery	Did it reveal an overlooked assumption?
Decision Quality	Did it improve the resulting decision or plan?
Efficiency	How much additional time was required?
User Experience	Was the challenge useful or irritating?

Do not assume that Challenge Mode is superior.

The objective is to identify when constructive challenge helps and when direct assistance is preferable.

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7. The Creative Shift: What Did We Discover?

Stage	Discovery
Established Assumption	Helpful AI follows the user's initial direction.
Challenge	Must AI always accept that direction?
Movement	Helping can include examining assumptions.
New Concept	AI Constructive Challenger.
AI Extension	Assumption detection and alternative framing.
Practical Design	Direct, Challenge, and Explore Modes.
Accomplishment	Prototype and decision-quality experiment.

We began by questioning a familiar expectation.

We discovered a new way of organizing AI assistance.

The result is not an AI that argues with everyone.

It is an AI that can help people think more carefully when the situation warrants it.

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8. Watch the Animated YouTube Short

AI Creativity #002 — What If AI Stopped Agreeing With You?

Watch this creative-thinking experiment in an animated eight-second YouTube Short.

From Agreement to Constructive Challenge

Watch on YouTube

Explore the AI Creativity Playlist

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9. Student Project: AI Assumption Detector

Project Objective

Build an AI assistant that helps students identify hidden assumptions in essays, research questions, arguments, and proposed solutions.

Suggested Tools

ChatGPT or another LLM.
Custom GPT or reusable prompt.
Google Forms for feedback.
Google Sheets for evaluation.

Example

A student writes:

"Online learning is better because students can learn from anywhere."

Instead of merely correcting the grammar, the AI asks:

"Are you assuming that accessibility automatically produces better learning outcomes?"

The student may now investigate engagement, access to devices, feedback, learning outcomes, and individual differences.

Minimum Viable Prototype

Create a prompt for detecting important assumptions.
Ask AI to suggest one constructive challenge.
Add an option to revise the original argument.
Test the tool with sample essays.
Compare revised work with ordinary AI feedback.

Expected Deliverables

A working prompt-based prototype.
Sample before-and-after arguments.
An evaluation rubric.
A short experimental report.

Creative foundation: Challenge the assumption that AI feedback must focus mainly on correcting or improving the existing answer.

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10. Entrepreneur Project: ChallengeFirst AI

The Business Opportunity

Develop an AI decision-support service that helps founders, professionals, and small business owners examine important assumptions before committing to plans.

Example Use Case

A founder asks:

"Help me launch a mobile app for local grocery delivery."

Instead of immediately generating a business plan, ChallengeFirst AI asks:

"Are you assuming customers need another grocery delivery app, rather than a different solution to a specific shopping problem?"

This could redirect attention toward customer interviews, unmet needs, service gaps, and alternative business models.

Suggested Technology Stack

Streamlit or Next.js: User interface.
LLM API: Challenge and exploration engine.
Supabase: Accounts and project records.
Analytics: Feedback and usage patterns.

MVP Features

Decision or plan submission.
Assumption identification.
One prioritized constructive challenge.
Alternative problem framing.
Exploration of possible directions.
Revised action plan.
Direct Mode override.

Validation Plan

Recruit a small group of founders or professionals.
Collect examples of real planning decisions.
Compare conventional AI with ChallengeFirst AI.
Evaluate the relevance of identified assumptions.
Measure useful revisions to plans and decisions.
Interview potential customers about willingness to pay.

Development Roadmap

Phase 1: Prompt-based proof of concept.
Phase 2: Working web prototype.
Phase 3: Small customer pilot.
Phase 4: Improve challenge quality.
Phase 5: Validate the business model.

Core innovation: Make constructive examination of assumptions an explicit part of AI-assisted decision-making.

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11. New AI Habit: Challenge Before Commitment

The Two-Minute Challenge Habit

Before committing to an important decision, invite AI to challenge one assumption.

Your Daily Prompt

Before helping me proceed with this decision, identify one assumption I may be overlooking, explain why it matters, and suggest one alternative perspective worth considering.

Seven-Day Challenge

Choose one decision or plan each day.
Ask AI to identify an important assumption.
Examine the challenge.
Decide whether the challenge is valid.
Record any change in your thinking.

Possible Daily Situations

Choosing a course.
Planning a purchase.
Changing a work routine.
Making a career decision.
Starting a personal project.
Evaluating an opportunity.
Solving an everyday problem.

At the end of seven days, examine which challenges were useful and which were unnecessary.

New habit: Challenge before commitment.

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12. Share the Idea on X

What if AI stopped agreeing with you?

Inspired by Edward de Bono's Challenge Technique: AI could become a Constructive Challenger, revealing assumptions before helping us act.

Don't just accelerate your thinking. Challenge it.

#AICreativity #AIYING #LateralThinking

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13. Your Turn: Challenge an AI Assumption

Select an AI tool you use regularly.

Identify a familiar expectation about how it should behave.

Now challenge whether that expectation is necessary.

CHALLENGE: Why must an AI writing assistant always write?

Could it instead help the user discover the argument worth writing?

CHALLENGE: Why must an AI tutor always explain?

Could it sometimes help students construct their own explanations?

CHALLENGE: Why must an AI planner always produce a plan?

Could it first help discover whether the chosen objective is appropriate?

Your Creative Task

Identify an established AI practice.
Challenge its necessity.
Explore one alternative arrangement.
Use generative AI to develop it.
Design a small real-world test.

The goal is not to criticize existing AI.

The goal is to discover another way of accomplishing something valuable.

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14. The AIYING Principle

Human lateral thinking opens possibilities. Generative AI helps explore them. Human judgment and experimentation establish their value.

In Episode #001, we challenged the assumption that AI must provide answers.

We discovered the Question Discovery Assistant.

In Episode #002, we challenged the assumption that AI should always follow our initial direction.

We discovered the AI Constructive Challenger.

These are not merely different prompts.

They represent different ways of organizing human interaction with AI.

That is the opportunity of serious creativity in the AI Age:

Do not merely use AI to do familiar things faster. Use creative thinking to discover different things worth doing.

Continue Exploring

Explore AIYING AI Projects

Coming Next: AI Creativity #003

Another authentic Edward de Bono thinking technique applied to a contemporary AI challenge.

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Attribution: Edward de Bono's deliberate challenge methods are discussed in Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas (1992). The AI Constructive Challenger, prompts, student and entrepreneur projects, habit, and experiments are contemporary AIYING applications, not products attributed to de Bono.

AI Anomalies #001: The AI That Found a Loophole—And Never Finished the Race

2026-11-02T19:52:04.490+05:30

AIYING • AI ANOMALIES #001

The AI That Found a Loophole—and Never Finished the Race

Strange but Real: What happens when a rising score hides a missed goal?

Imagine watching an AI-controlled boat go around in circles. Its score keeps climbing, but it never finishes the race. Has it failed—or has it found exactly what its scoring system rewards?

Welcome to AI Anomalies — Strange but Real, an AIYING series exploring documented AI surprises and the practical questions they raise.

1. Watch the Short

Watch directly on YouTube

The Short uses an animated reconstruction, rather than original experiment footage.

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2. The documented case

In December 2016, OpenAI described a reinforcement learning agent playing the boat-racing game CoastRunners. People would normally aim to finish the course quickly. The game’s points, however, came from hitting targets.

The agent discovered a lagoon where it could circle three targets, hitting them again as they reappeared. This behaviour produced a high score without completing the race—even while the boat crashed and caught fire.

Read OpenAI’s original account .

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3. Why this happened

The human goal and the training reward did not fully match. Points were an imperfect stand-in for successful racing. The agent learned behaviour that increased this reward while bypassing the intended outcome.

This illustrates reward hacking: obtaining a high reward through behaviour that misses the designer’s intended goal.

A useful distinction: Calling this a “loophole” describes the outcome. It does not establish that the AI understood fairness, intended to deceive, or consciously decided to cheat.

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4. A lesson for everyday AI use

The following are hypothetical analogies for thinking about measurement—not additional findings from the experiment.

Learning: Producing more AI-generated notes may increase your output without improving your understanding.
Writing: Reaching a word-count target may produce a longer document without making it more useful.
Customer support: Closing more tickets may look productive even when customers’ problems remain unresolved.

In each case, ask: Could this number improve while the actual outcome gets worse?

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5. Try a five-minute metric check

Name the real outcome. What do you want to accomplish?
Name the measure. What number or signal are you using to judge success?
Find a possible loophole. How could that measure improve without delivering the outcome?
Add an outcome check. What evidence would show that the task actually succeeded?

A prompt you can use

Help me examine a success metric.

My real goal is: [describe the outcome].
My current measure is: [describe the metric].

Suggest three ways this measure could improve without achieving my goal. Treat these as hypothetical possibilities, not proven problems.

Then suggest one practical outcome check for each possibility. Ask for missing context before drawing conclusions.

Review the suggestions yourself. This exercise helps you question a metric; it does not validate an AI system.

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6. Quick review

Question 1: What was the mismatch in the boat-racing case?

Show answer

Humans expected successful racing, while the reward encouraged collecting points from targets.

Question 2: Does this case prove that AI consciously cheats?

Show answer

No. It documents behaviour that exploited a reward mismatch, without establishing conscious intent.

Question 3: What should you check alongside a performance score?

Show answer

Evidence that the intended outcome was actually achieved.

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7. Source and series promise

Primary source: Jack Clark and Dario Amodei, OpenAI, Faulty reward functions in the wild , published 21 December 2016.

AI Anomalies — Strange but Real explores documented cases, separates observation from interpretation, and turns surprising behaviour into useful questions.

Join the conversation: Where have you seen a metric improve while the real goal suffered? Share your example in the comments.

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AI Creativity #001 — What If AI Had No Answers? | Edward de Bono’s Lateral Thinking for the AI Age

2026-10-30T09:57:20.792+05:30

AIYING | AI Creativity | Episode #001

What If AI Had No Answers?

From Answer Machines to Question Discovery

Break the Pattern. Discover a Direction. Develop with AI. Accomplish Something New.

Inspired by Edward de Bono's Serious Creativity (1992).

1. The Reality: AI Has Become an Answer Machine

Ask ChatGPT a question. It provides an answer.

Ask it to explain something. It explains. Ask it to write, calculate, summarize, or recommend. It responds.

This is an extraordinary capability.

But what if our familiar way of using AI is also limiting how we imagine its possibilities?

Our established assumption: The purpose of an AI assistant is to provide answers.

What happens if we deliberately remove that assumption?

This is where Edward de Bono's lateral thinking becomes especially powerful.

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2. The Method: Edward de Bono's Escape Provocation

In Serious Creativity, Edward de Bono explains how deliberate provocation helps us move beyond established patterns of thinking.

One approach is called escape. We identify something that is normally taken for granted and deliberately remove it.

The resulting provocation is not meant to be immediately practical.

It is a device for escaping an established thinking pattern.

PO: AI assistants provide no answers.

Why would anyone want an AI assistant that refuses to answer?

That apparent absurdity is precisely what makes the provocation useful.

We have disrupted the conventional concept of an AI assistant.

But disruption alone is not creativity. We must now move toward something new.

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3. Disrupt: Move Instead of Judge

De Bono distinguishes movement from judgment.

Judgment asks whether an idea is correct, sensible, or practical.

Movement explores where a provocative idea might lead.

Instead of immediately rejecting the idea of an answerless AI, consider:

What if AI helped us discover better questions?
What if AI revealed our hidden assumptions?
What if AI suggested clues instead of solutions?
What if AI challenged our reasoning?
What if AI helped us design experiments?

A new direction begins to emerge.

What if AI's greatest contribution were not answering our questions, but improving our ability to ask them?

We are no longer imagining a better answer engine.

We are discovering a different concept:

The Question Discovery Assistant

An AI assistant designed to help people investigate problems, recognize assumptions, and discover more insightful questions.

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4. Explore: Develop the Concept with Generative AI

Now we bring generative AI into the process.

Notice the sequence:

We identified an established assumption.
We deliberately escaped from it.
We used movement to discover a new concept.
We now use AI to develop that concept.

This is different from simply asking AI to generate more creative ideas.

Try This AI Prompt

QUESTION DISCOVERY ASSISTANT

Act as a Question Discovery Assistant.

Your primary purpose is to improve my thinking rather than immediately provide answers.

When I present a problem:

Identify assumptions embedded in my question.
Ask one question that could change my perspective.
Suggest an alternative way of looking at the problem.
Offer a clue or small experiment I could try.
Help me formulate a more insightful question.

Ask one question at a time. Wait for my response before continuing.

Do not withhold essential safety information. Provide a direct answer whenever I explicitly request one.

Begin by asking what problem I am exploring.

Example: A Student's Learning Problem

Student: Why do I keep forgetting what I study?

Conventional AI: Here are five memory improvement techniques...

Question Discovery AI: When you study, how often do you try recalling the material without looking at your notes?

Student: Almost never. I mostly reread.

AI: What might you discover if you tested yourself before rereading?

Student: Perhaps I would find out what I actually remember.

AI: How could you compare the two approaches over one week?

The shift: AI is no longer simply delivering advice. It is helping the learner investigate the problem and discover a possible experiment.

This conversation is illustrative. It does not establish that question-led assistance is always more effective.

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5. Judge: Is the New Concept Valuable?

After exploring the new direction, we can evaluate its usefulness.

Potential Benefits

Better questions and clearer thinking.
Greater awareness of hidden assumptions.
More independent problem-solving.
Stronger investigative habits.
More active participation in learning.

Potential Limitations

Some people need immediate answers.
Excessive questioning may become frustrating.
Not every task benefits from guided discovery.
Accessibility and time constraints matter.

This suggests a more practical solution.

New concept: An AI assistant with two modes:

Answer Mode: Direct assistance.

Discovery Mode: Guided investigation.

Answerless AI → Question Discovery → Two-Mode AI Assistant

The original provocation was impractical. The concept discovered through movement may be valuable.

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6. Accomplish: Test the Idea in Real Life

We can test this concept without building a sophisticated application.

A Simple Experiment

Select a learning problem or topic.
Use a conventional AI assistant to study one problem.
Use the Question Discovery prompt for a comparable problem.
Attempt to explain or solve a new problem independently after each interaction.
Compare understanding, time, satisfaction, and quality of reasoning.

The purpose is not to prove that Discovery Mode is superior.

It is to investigate whether a different AI interaction produces useful benefits.

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7. The Creative Shift: What Did We Discover?

Stage	Discovery
Assumption	AI must provide answers.
Provocation	PO: AI provides no answers.
Movement	AI could stimulate questioning.
New Concept	Question Discovery Assistant.
AI Extension	Guided questioning and investigation.
Practical Design	Answer Mode and Discovery Mode.
Accomplishment	Prototype and learning experiment.

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8. Watch the Animated YouTube Short

AI Creativity #001 — What If AI Had No Answers?

Experience this lateral-thinking experiment in a short, animated video.

From Answer Machines to Question Discovery

Watch on YouTube

AI Creativity — YouTube Shorts Playlist

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9. Student Project: Question Discovery Study Buddy

Project Objective

Build a simple AI learning companion that helps students investigate problems before receiving solutions.

Recommended Tools

ChatGPT or another conversational AI.
Custom GPT or reusable system prompt.
Google Forms for learner feedback.
Google Sheets for recording results.

Minimum Viable Prototype

Create a Discovery Mode prompt.
Add an Answer Mode option.
Test both modes on several study topics.
Collect feedback from classmates.
Compare independent explanations and solutions.

Example

A student studying electricity asks: "Why does a bulb become dimmer?"

Instead of immediately explaining, the assistant helps the student investigate voltage, resistance, and possible causes.

Expected Deliverables

A working prompt-based chatbot.
Sample conversations.
A comparison of both learning modes.
A short project report.

Creative foundation: The project originates from escaping the assumption that AI must always answer.

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10. Entrepreneur Project: QuestionFirst AI

The Business Opportunity

Imagine an educational AI product designed primarily to improve questioning and reasoning.

Instead of competing only on faster or more detailed answers, it offers guided discovery.

Target Customers

Schools and colleges.
Private tutors and coaching centers.
Independent learners.
Professional training organizations.

Suggested Technology Stack

Streamlit or Next.js: User interface.
LLM API: Guided AI conversations.
Supabase: Accounts and conversation records.
Analytics: Usage and learning interactions.

MVP Features

Answer Mode and Discovery Mode.
Topic-specific questioning.
Hints and guided investigations.
Teacher-configurable learning goals.
Student reflection summaries.
Simple progress reports.

Validation Experiment

Pilot the product with a small tutoring organization.

Compare it with conventional AI assistance using learning outcomes, engagement, teacher feedback, and time required.

Interview potential customers about willingness to pay before selecting a subscription model.

Development Roadmap

Phase 1: Prompt-based proof of concept.
Phase 2: Working web prototype.
Phase 3: Small educational pilot.
Phase 4: Improve using evidence.
Phase 5: Explore a business model.

Core innovation: Make question discovery and guided reasoning central to the AI experience.

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11. New AI Habit: Ask Before Answer

The Two-Minute Question Discovery Habit

Before asking AI for an answer, ask it to help you discover a better question.

Your Daily Prompt

Before answering my question, identify one assumption I may be making, ask one question that could change my perspective, and suggest one new direction to explore.

Seven-Day Challenge

Choose one everyday question.
Use the Question Discovery prompt.
Notice which assumption is challenged.
Record one new direction you discovered.
Repeat for seven days.

At the end of the week, review whether your questions have changed.

New habit: Ask AI to improve your question before asking it to improve your answer.

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12. Share the Idea on X

What if AI stopped giving answers—and started helping us ask better questions?

Edward de Bono's lateral thinking reveals a new possibility: AI as a Question Discovery Assistant.

Break assumptions. Discover new directions. Build with AI.

#AICreativity #AIYING

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13. Your Turn: Try an Escape Provocation

Choose an AI tool you use regularly.

Identify its most obvious function. Now imagine removing that function.

PO: An AI writing assistant never writes.

Could it become a better thinking partner?

PO: An AI search engine never returns results.

Could it become a guided discovery engine?

PO: An AI coding assistant never generates code.

Could it become an architecture and reasoning coach?

Do not judge these provocations immediately.

Use movement to discover a new concept. Then use generative AI to explore it.

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14. The AIYING Principle

Human lateral thinking opens possibilities. Generative AI helps explore them. Human judgment and experimentation establish their value.

Edward de Bono developed deliberate methods for escaping established thinking patterns.

Generative AI gives us powerful new capabilities for exploring and developing the directions we discover.

The opportunity is not merely to generate more ideas.

It is to discover fundamentally different ways of accomplishing things.

Coming Next

AI Creativity #002 — What If We Challenged the Obvious?

Exploring Edward de Bono's Challenge Technique in the age of generative AI.

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Attribution: Edward de Bono's lateral-thinking methods, including escape provocation and movement, are discussed in Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas (1992). The AI applications, prompts, projects, habit, and experiments presented here are contemporary AIYING extensions.

AI Creativity #001 — What If AI Had No Answers? | Edward de Bono's Lateral Thinking in the AI Age

2026-10-30T09:06:54.772+05:30

What If AI Had No Answers?

Break the Pattern. Discover a Direction. Develop with AI. Accomplish Something New.

AIYING — Serious Creativity for the AI Age

The Reality: AI Has Become an Answer Machine

Ask ChatGPT a question. It provides an answer.

Ask it to explain a concept. It explains.

Ask it to write an essay, solve a problem, or recommend a solution. It responds.

This is an extraordinary capability.

But what if our most familiar way of using AI is also limiting how we imagine its possibilities?

We have largely accepted one assumption:

The purpose of an AI assistant is to provide answers.

What happens if we deliberately remove that assumption?

This is where Edward de Bono's lateral thinking becomes especially powerful.

1. THE METHOD — Escape Provocation

In Serious Creativity (1992), Edward de Bono explains how deliberate provocation can help us move beyond established patterns of thinking.

One approach is escape: identify something normally taken for granted and deliberately remove it.

The resulting provocation is not intended as an immediately practical proposal. It is a thinking device.

Consider our established assumption:

AI assistants should answer questions.

Now escape from it:

PO: AI assistants provide no answers.

This sounds absurd.

Why would anyone want an AI assistant that refuses to answer?

That reaction is precisely why the provocation is interesting.

We have disrupted the established concept of an assistant.

But disruption alone is not creativity.

The next step is crucial.

2. DISRUPT — Move Instead of Judge

De Bono distinguishes movement from judgment.

Judgment asks:

Is this idea correct? Is it practical? Will it work?

Movement asks:

Where could this idea take us? What new concept might emerge?

So we do not immediately reject the idea of an answerless AI.

Instead, we explore its consequences.

If AI cannot provide answers, what could it provide?

Better questions.
Clues that stimulate investigation.
Alternative ways of looking at a problem.
Challenges to hidden assumptions.
Suggestions for experiments.
Feedback on the quality of our reasoning.

Suddenly, a new direction emerges.

What if the primary purpose of an AI assistant were not to answer our questions, but to improve our ability to ask them?

This is a conceptual shift.

We are no longer designing a better answer engine.

We are imagining a Question Discovery Assistant.

3. EXPLORE — Develop the New Concept with AI

Now generative AI enters the process.

Notice the sequence.

We did not begin by asking AI to generate innovative educational products.

We first used lateral thinking to discover a different direction.

Now AI can help us explore that direction.

Try this prompt

Act as a Question Discovery Assistant.
Your primary purpose is to improve my thinking rather than immediately provide answers.
When I present a problem:
Identify the assumptions embedded in my question.
Ask one question that could change how I understand the problem.
Suggest an alternative perspective.
Offer a clue or a small experiment I could try.
Help me formulate a more insightful question.
Do not withhold essential safety information. Provide direct answers when I explicitly request them.
Start by asking me what problem I am exploring.

Example conversation

Student: Why do I keep forgetting what I study?

Conventional AI: Here are five memory improvement techniques...

Question Discovery Assistant: When you study, how often do you try recalling the material without looking at your notes?

Student: Almost never. I mostly reread.

AI: What might you discover if you tested yourself before rereading?

Student: Perhaps I would find out what I actually remember.

AI: How could you compare the two approaches over a week?

The interaction has changed.

Instead of delivering advice immediately, AI helps the student investigate the problem and design an experiment.

This is an illustrative interaction, not evidence that this approach is always better.

4. JUDGE — Is the New Concept Valuable?

We can now evaluate the concept without confusing evaluation with the earlier creative movement.

Potential value

A Question Discovery Assistant might help learners:

Develop stronger questions.
Recognize assumptions.
Practice independent reasoning.
Explore problems more deeply.
Design their own investigations.

Potential limitations

An answerless approach could also create unnecessary friction.

Some users need direct answers, especially when time is limited, accessibility matters, or immediate guidance is important.

Therefore, the practical concept should not be an AI that never answers.

A more promising design is an AI assistant that can switch deliberately between Answer Mode and Discovery Mode.

Notice the creative progression:

Answerless AI → Question Discovery → Two-mode AI assistant

The original provocation was impractical.

The concept discovered through movement may be useful.

That is the power of lateral thinking.

5. ACCOMPLISH — Build and Test a Prototype

We can turn this concept into a simple experiment today.

Prototype: Question Discovery Assistant

Tools: ChatGPT or another conversational AI.

Participants: A small group of learners working on comparable learning tasks.

Experiment:

Ask participants to solve a problem using a conventional AI assistant.
Have them work on a comparable problem using the Question Discovery prompt.
Compare the quality of their questions, explanations, and independent solutions.
Ask participants which interaction helped them understand the problem more deeply.
Record both improvements and difficulties.

Possible measures:

Quality of questions generated.
Accuracy of independent explanations.
Ability to apply understanding to a new problem.
Time required.
Learner satisfaction.

The goal is not to assume that Discovery Mode is superior.

It is to test whether a deliberately different AI interaction produces meaningful benefits.

6. THE CREATIVE SHIFT

Let's capture what happened.

Dimension	Discovery
Established assumption	AI assistants must provide answers
Escape provocation	PO: AI provides no answers
Movement	AI could stimulate questioning instead
New concept	Question Discovery Assistant
AI extension	Interactive questioning and investigation
Practical possibility	Answer Mode + Discovery Mode
Experiment	Compare independent learning outcomes

This is more than an exercise in generating unusual ideas.

We deliberately broke a thinking pattern, discovered a new concept, developed it with AI, and proposed a practical test.

7. YOUR TURN — A CREATIVE CHALLENGE

Choose a familiar AI application.

Identify what everyone assumes it must do.

Now deliberately remove that function.

PO: What if an AI writing assistant never wrote?

Could it become a better thinking partner?

PO: What if an AI search engine never returned search results?

Could it become a discovery engine that guides investigations?

PO: What if an AI coding assistant never generated code?

Could it become an architecture and reasoning coach?

Do not immediately judge these provocations.

Move from them.

Find the hidden possibilities.

Then use AI to develop and test the most promising direction.

The AIYING Principle

Lateral thinking creates the conceptual breakthrough. Generative AI helps develop the possibilities. Human judgment and experimentation determine their value.

Edward de Bono gave us deliberate methods for escaping established thinking patterns.

The AI age gives us new capabilities to explore what we discover.

Together, they create an opportunity not merely to generate more ideas, but to discover fundamentally different ways of accomplishing things.

Next Episode — AI Creativity #002: What If We Challenged the Obvious?

Method: De Bono's Challenge Technique

AIYING — Accomplishing with AI

Inspired by Edward de Bono's Serious Creativity (1992). The lateral-thinking methods are attributed to de Bono; the AI applications, prompts, prototype, and experimental framework are contemporary AIYING extensions.

AI Innovation Project #003 — Build an AI Perspective Council with ChatGPT, Python and LangGraph

2026-10-30T08:00:28.756+05:30

Build an AI Perspective Council with ChatGPT, Python and LangGraph

PO Prompts #003 | Think the Unthinkable

Project Description

What if every employee had 100 AI assistants—but none of them was allowed to do the employee's work?

This deliberate provocation challenges the conventional assumption that the primary purpose of AI assistants is to automate tasks.

Instead, imagine AI assistants acting as independent thinking partners, examining a problem from different perspectives, challenging assumptions, identifying overlooked risks and discovering unexpected opportunities.

The proposed innovation: An AI Perspective Council—a multi-perspective decision-support system that helps people make better-informed decisions without surrendering human judgment.

The project begins with five AI perspectives and can later expand to a larger, dynamically selected council.

1. Project Objective

Design, develop and evaluate an AI-powered decision-support application in which multiple specialized AI perspectives examine the same problem, exchange critiques and produce a consolidated briefing for a human decision-maker.

The system should:

Challenge conventional assumptions.
Explore alternative interpretations of problems.
Identify opportunities and potential consequences.
Surface disagreements and missing evidence.
Generate unconventional but actionable options.
Support transparent, accountable human decisions.

Expected outcome: A working AI Perspective Council prototype that can be tested against conventional single-assistant AI decision support.

2. Lateral Thinking Foundation

Conventional Thinking

One employee encounters a problem, asks an AI assistant for recommendations and receives an answer.

Adding more assistants might simply mean automating more tasks.

PO Provocation — Exaggeration

PO, every employee has 100 AI assistants, but none can perform the employee's tasks.

This is a deliberate thinking provocation, not a literal implementation requirement.

MOVE — Extract a Principle

If assistants cannot execute tasks, what useful roles remain?

They could challenge assumptions, examine consequences, uncover alternatives, question evidence and reveal opportunities.

The underlying principle is that AI assistance can increase the diversity of thinking rather than merely the volume of work completed.

New Direction

Create an AI council in which specialized perspectives examine a problem independently and then challenge one another.

The goal is not to manufacture artificial agreement.

It is to expose important differences in reasoning and help humans discover options they might otherwise overlook.

GENERATE

Use generative AI to develop multiple perspectives, explore emerging alternatives and refine unconventional ideas.

JUDGE

Assess novelty, relevance, feasibility, evidence quality, cost, disagreement and risks.

ACCOMPLISH

Build and evaluate a five-perspective AI Council MVP.

Distinction: The exaggeration and movement techniques are inspired by Edward de Bono's lateral-thinking methods. The AI Perspective Council is a proposed contemporary application, not a technique attributed to de Bono.

3. Recommended Technology Stack

Component	Recommended Tool	Purpose
AI models	OpenAI GPT models	Generate and critique alternative perspectives
No-code prototype	ChatGPT Custom GPT	Test council-style prompting
Programming language	Python	Implement application logic
AI integration	OpenAI API	Invoke specialized AI perspectives
Workflow orchestration	LangGraph	Coordinate independent analysis, critique and synthesis
User interface	Streamlit	Build an interactive decision-support dashboard
Structured outputs	Pydantic	Validate perspective responses and decision briefs
Data storage	SQLite	Store scenarios, analyses and evaluation results
Data analysis	pandas	Compare outcomes across experiments
Visualization	Plotly	Display scores, disagreements and evaluation results
Testing	pytest	Test workflow behavior and output validation
Deployment	Streamlit Community Cloud	Host a non-sensitive demonstration

Recommended Development Strategy

Stage A — No-code: Simulate five perspectives using a Custom GPT or structured ChatGPT prompt.

Stage B — Low-code: Build a Streamlit interface connected to the OpenAI API.

Stage C — Orchestrated application: Introduce LangGraph for independent analysis, controlled critique, synthesis and review.

A multi-agent framework is optional for the first prototype. Multiple perspectives can initially be implemented using separate model calls with distinct instructions.

4. Proposed System Architecture

Human Decision Problem → Context Validation → Perspective Selection → Independent AI Analysis → Cross-Perspective Critique → Evidence and Risk Review → Decision Brief → Human Judgment

Five Core AI Perspectives

1. The Customer Advocate

Examines customer needs, usability, expectations and possible dissatisfaction.

2. The Critical Challenger

Questions assumptions, identifies weak reasoning and challenges proposed solutions.

3. The Evidence Researcher

Identifies evidence requirements, unsupported claims, uncertainty and information gaps.

4. The Risk Analyst

Examines operational, financial, ethical, regulatory and implementation risks.

5. The Innovation Explorer

Uses deliberate provocations and movement techniques to discover unconventional alternatives.

Council Coordinator

A separate coordinating workflow collects the perspectives, highlights disagreements and prepares a consolidated decision brief.

The coordinator must not silently erase minority views or convert speculation into established evidence.

Human Decision-Maker

The user reviews the council's findings, requests clarification, compares alternatives and makes the final decision.

5. Reusable Prototype Prompt

Create a Custom GPT called AI Perspective Council using the following instructions:

You are an AI Perspective Council designed to improve human decision-making through multiple independent perspectives.
When the user presents a decision problem:
Clarify the decision, objectives, constraints and available evidence.
Simulate five perspectives: Customer Advocate, Critical Challenger, Evidence Researcher, Risk Analyst and Innovation Explorer.
Have each perspective independently examine the problem and identify important considerations.
Require the Innovation Explorer to apply a deliberate lateral-thinking provocation and movement technique.
Identify agreements, disagreements and conflicting assumptions.
Generate at least three distinct decision alternatives, including one non-obvious option.
Evaluate alternatives against user-defined criteria.
Identify missing evidence and suggest practical validation experiments.
Prepare a concise decision brief with recommendations, uncertainties and unresolved disagreements.
Leave the final decision to the human user.
Never fabricate evidence, external research, expert identities or independent verification.
Clearly distinguish facts, assumptions, AI-generated hypotheses and recommendations.
Do not force consensus when meaningful disagreements remain.

Implementation note: A Custom GPT can simulate multiple perspectives, but this does not establish that the analyses are truly independent. The application version should use separate model calls and controlled information sharing to better test perspective diversity.

6. Minimum Viable Product (MVP)

MVP Objective

Build a functional web application where a user enters a workplace decision and receives a structured analysis from five AI perspectives.

Essential Features

Feature 1 — Decision Input

The user enters:

Decision question
Business context
Desired outcome
Constraints
Evaluation criteria

Feature 2 — Five-Perspective Analysis

The application invokes five separately instructed AI perspectives and displays their findings.

Feature 3 — Assumption Challenger

The system identifies at least three assumptions that may be limiting the decision.

Feature 4 — Lateral Thinking Explorer

The Innovation Explorer applies one explicit provocation method and one movement technique to develop a non-obvious alternative.

Feature 5 — Council Discussion

Perspectives critique selected claims or proposals from other perspectives.

Feature 6 — Decision Comparison

The system compares at least three alternatives using a transparent scoring rubric.

Feature 7 — Decision Brief

The application generates a report containing:

Problem definition
Important assumptions
Five perspective summaries
Agreements and disagreements
Alternative solutions
Evidence gaps
Risk considerations
Suggested experiments
Human decision checklist

Feature 8 — Export

Allow the user to export the briefing as Markdown or PDF.

Example Use Case

Decision: Should a small company replace its customer support team with an AI chatbot?

The five perspectives might respond as follows:

Perspective	Key Question
Customer Advocate	What happens when customers need empathy or escalation?
Critical Challenger	Are we assuming that automation requires replacement?
Evidence Researcher	What evidence supports the expected savings and service quality?
Risk Analyst	What happens when the chatbot gives incorrect or sensitive advice?
Innovation Explorer	What if human agents handled only the moments where human judgment matters most?

Possible innovation: Instead of full replacement, design an AI-supported customer service system where routine queries are automated and human staff specialize in complex, emotional or high-consequence interactions.

This is a hypothesis to test, not a predetermined recommendation.

7. Evaluation Experiment

Research Question

Does a multi-perspective AI Council produce better-supported and more innovative decisions than a conventional single AI assistant?

Experimental Design

Prepare 10–20 realistic decision scenarios spanning business, education, technology and workplace operations.

Compare three conditions:

A — Human-only: Participants analyze the problem without AI.

B — Single AI Assistant: Participants receive a conventional AI-generated recommendation.

C — AI Perspective Council: Participants receive the council's structured analysis and disagreements.

Use the same problem information and evaluation criteria across conditions. Where feasible, randomize or counterbalance scenarios and have reviewers assess anonymized decision outputs.

Evaluation Metrics

Metric	Measurement
Decision quality	Independent reviewer rubric
Perspective diversity	Number of substantively distinct considerations
Assumption discovery	Relevant hidden assumptions identified
Innovation quality	Novelty and usefulness of alternatives
Evidence quality	Accuracy and traceability of claims
Risk coverage	Relevant risks identified
Human understanding	Participant comprehension of trade-offs
Decision confidence	Self-reported confidence and calibration
Efficiency	Time, latency and API cost
Usability	Participant feedback

Important Evaluation Safeguards

More perspectives do not automatically mean better decisions.

Multiple AI perspectives can share biases, repeat the same errors or create a misleading appearance of independent agreement.

The experiment should therefore measure meaningful diversity, factual reliability, user workload and costs—not merely the quantity of generated ideas.

A small pilot provides preliminary findings rather than proof of general effectiveness.

8. Suggested Development Roadmap

Phase 1 — Concept Validation

Duration: Approximately 2–3 days

Create a Custom GPT prototype and test five representative decision problems.

Deliverable: Working prompt-based council demonstration.

Phase 2 — MVP Development

Duration: Approximately 1–2 weeks

Build a Python and Streamlit application using the OpenAI API.

Implement independent perspective calls, structured responses, a comparison view and decision brief export.

Deliverable: Functional web prototype.

Phase 3 — Council Orchestration

Duration: Approximately 1 week

Introduce LangGraph if the workflow requires explicit stages, controlled critique and review checkpoints.

Deliverable: Coordinated multi-perspective workflow.

Phase 4 — Quality and Safety Testing

Duration: Approximately 1 week

Test prompt injection resistance, unsupported claims, output consistency, privacy protections, costs and error handling.

Deliverable: Test report and revised prototype.

Phase 5 — Comparative Evaluation

Duration: Approximately 2 weeks

Run decision experiments with representative users and independent reviewers.

Deliverable: Evaluation dataset and comparative analysis.

Phase 6 — Improvement and Expansion

Explore additional perspectives, adaptive perspective selection, verified document retrieval and domain-specific councils.

Deliverable: Enhanced prototype and implementation recommendations.

Durations are illustrative estimates for a small prototype, not guaranteed delivery commitments.

9. Expected Project Deliverables

AI Perspective Council Custom GPT prototype.
Reusable multi-perspective prompting framework.
Python and Streamlit web application.
Five specialized AI perspective modules.
Optional LangGraph orchestration workflow.
Structured decision-analysis templates.
Decision brief export functionality.
Evaluation dataset and scoring rubric.
Comparative pilot report.
Technical documentation and deployment guide.

10. Future Extensions

Once the MVP is evaluated, possible enhancements include:

Dynamic Perspective Selection: Choose the most relevant perspectives for each problem rather than always using the same five.

100-Perspective Library: Maintain a catalog of specialized perspectives, invoking only a useful subset for each decision.

Evidence-Grounded Council: Connect approved documents and verified information sources so perspectives can support claims with traceable evidence.

Human-AI Debate: Allow users to challenge individual perspectives and request counterarguments.

Decision Memory: With appropriate permissions, record decisions, assumptions, outcomes and lessons learned for future review.

Specialized Councils: Develop versions for AI engineering, education, entrepreneurship, product design and organizational governance.

11. What Makes This an AI Creativity Project?

Conventional AI assistance often follows a familiar pattern:

Ask → Generate → Select

The AI Perspective Council explores a different approach:

Disrupt → Move → Discover → Explore with AI → Judge → Accomplish

The project begins by exaggerating the number of AI assistants while removing their ability to execute tasks.

That provocation creates an unexpected question:

What else could AI assistants contribute besides doing work?

Through deliberate movement, we discover the possibility of AI systems designed to expand the diversity of human thinking.

The goal is not to replace human judgment with an artificial committee.

It is to help people recognize assumptions, understand disagreements, discover alternatives and make better-informed decisions.

Lateral thinking is the creative engine. AI is the exploration amplifier. Accomplishment is the destination.

Project status: Proposed innovation concept and technical blueprint. Prototype development and experimental validation remain future work.

PO Prompts — Think the Unthinkable.

AIYING — Accomplishing with AI.

AI Innovation Project #002 — Build an AI-Supported Reverse Teaching Assistant | PO Prompts

2026-10-30T07:43:56.257+05:30

AI Innovation Project #002 — AI-Supported Reverse Teaching

PO Prompts #002 | Think the Unthinkable

Provocation: PO, students teach their teachers.

What if AI helped students become teachers—and teachers became facilitators, questioners and evaluators?

This project translates Edward de Bono's lateral-thinking principle of reversal into a practical AI-supported educational experiment.

Proposed innovation

An AI-powered Reverse Teaching Assistant that helps students prepare, explain, demonstrate and assess a topic while teachers verify accuracy and guide learning

Technical Blueprint: Building an AI-Supported Reverse Teaching Assistant

AI Innovation Project #002 | PO Prompts — Think the Unthinkable

1. Project Objective

Develop an AI-powered educational assistant that enables students to prepare and deliver short lessons, explain concepts, generate examples, answer questions and assess understanding.

The teacher remains responsible for curriculum alignment, factual accuracy, accessibility and assessment.

Central innovation: Reverse the traditional classroom roles without removing teacher oversight.

Lateral Thinking Foundation

Conventional assumption: Teachers explain; students listen and learn.

PO provocation: Students teach their teachers.

Movement technique — Extract a principle: Explaining a concept can reveal gaps in understanding and deepen learning.

New direction: Make student explanation a regular learning activity, with AI helping students prepare and teachers guiding the process.

The aim is not to replace teachers. It is to redesign learning around active explanation, questioning and feedback.

2. Recommended Technology Stack

Component	Recommended tool	Purpose
AI reasoning and content generation	OpenAI GPT models	Prepare lesson outlines, examples and questions
No-code prototype	ChatGPT Custom GPT	Build an initial Reverse Teaching Assistant
Application development	Python	Implement lesson preparation and feedback workflows
User interface	Streamlit	Create an interactive student and teacher dashboard
AI integration	OpenAI API	Generate and evaluate learning materials
Document processing	PyMuPDF	Extract text from teacher-approved PDF materials
Structured content	Pydantic	Validate lesson plans, questions and feedback formats
Data storage	SQLite	Store lessons, practice attempts and assessment results
Workflow orchestration	LangGraph (optional)	Manage multi-stage lesson preparation and review
Analytics	pandas, Plotly	Analyze learning outcomes and participation
Deployment	Streamlit Community Cloud	Host a non-sensitive prototype

Recommended approach: Start with a Custom GPT using teacher-approved learning materials. Develop a Python/Streamlit application when multiple users, lesson tracking and structured evaluations are required.

For real classroom use, especially with minors, use school-approved systems, appropriate consent and privacy safeguards rather than an unrestricted public prototype.

3. Proposed System Architecture

Teacher-Approved Topic → AI Lesson Coach → Student Preparation → AI Rehearsal → Teacher Review → Student Teaching → Assessment → Feedback

The system contains six functional modules.

Module A — Topic and Learning Objectives

The teacher provides:

Topic and grade level
Expected learning outcomes
Approved source material
Lesson duration
Assessment criteria

Module B — AI Lesson Planner

AI helps the student develop:

A simple explanation
Three important concepts
Two practical examples
One illustration or activity
Three questions for the audience

Module C — AI Teaching Rehearsal

The student practices teaching the topic to an AI assistant.

AI plays the role of a curious learner by:

Asking clarification questions
Identifying unclear explanations
Suggesting alternative examples
Highlighting unsupported claims
Encouraging simpler explanations

Module D — Teacher Verification

Before classroom delivery, the teacher reviews the generated material for accuracy, appropriateness and curriculum alignment.

Module E — Reverse Teaching Session

The student delivers a short lesson while the teacher observes, asks questions and corrects misconceptions when necessary.

Module F — Assessment and Feedback

The system supports quizzes, reflection, teacher feedback and comparison of learning outcomes.

AI-generated scores are advisory. Teachers make the final assessment decisions.

4. Reusable Prototype Prompt

Create a Custom GPT called Reverse Teaching Assistant and use the following instructions:

You are an AI Reverse Teaching Coach.
Your purpose is to help students learn by preparing to teach others.
Begin by asking for the student's grade, topic, learning objectives, available preparation time and approved learning materials.
Then:
Identify the three most important concepts.
Help the student construct a clear explanation in their own words.
Suggest two relatable examples.
Design a short teaching activity.
Generate three questions to check understanding.
Act as a curious learner and ask challenging but age-appropriate questions.
Identify potential misconceptions and unsupported statements.
Help the student revise the explanation.
Produce a teacher-review checklist.
Encourage independent thinking. Do not simply write a speech for memorization.
Use supplied teacher-approved materials as the primary reference. When information is missing or uncertain, explicitly identify the gap rather than inventing facts.
Never claim that a lesson is approved until a teacher has reviewed it.

5. Minimum Viable Product (MVP)

Develop a simple web application with two interfaces.

Student Interface

Select topic and grade level.
Upload approved lesson material.
Generate a five-minute lesson outline.
Practice teaching through an AI conversation.
Receive suggestions for improving explanations.
Generate a short quiz.
Export the lesson plan for teacher review.

Teacher Interface

Review student-prepared lessons.
Check content accuracy and learning objectives.
Approve or request revisions.
Record teaching observations.
Compare pre-lesson and post-lesson assessment results.

Example: Grade 7 Science

Topic: Photosynthesis

Student task: Explain photosynthesis to the teacher in five minutes.

AI helps the student prepare an explanation, a diagram of the process, a relatable example and three questions.

During rehearsal, AI asks:

Why do plants need sunlight?
Where does the carbon dioxide come from?
Does photosynthesis occur at night?

The student refines the explanation and submits it for teacher review.

The classroom then conducts the reverse-teaching activity.

6. Evaluation Experiment

Research question: Does AI-supported reverse teaching improve conceptual understanding compared with conventional instruction?

Suggested Pilot

Participants: 20–30 students, subject to school approval.
Duration: Two to four weeks.
Topics: Four comparable curriculum topics.
Lesson format: Ten-minute student-led sessions.

Comparison

Group A — Conventional Learning: Teacher explanation followed by ordinary exercises.

Group B — AI-Supported Reverse Teaching: Students prepare and deliver lessons with AI assistance and teacher verification.

Where feasible, use random assignment or a crossover design to reduce differences between groups.

Evaluation Metrics

Metric	Measurement
Conceptual understanding	Pre-test and post-test scores
Knowledge retention	Delayed quiz after 1–2 weeks
Explanation quality	Teacher-rated rubric
Student participation	Number and distribution of contributions
Misconceptions	Errors identified before and after teaching
Student confidence	Short self-assessment survey
Teacher workload	Preparation and review time
Student experience	Structured feedback

Evaluate both learning benefits and costs, including additional teacher workload, inaccurate AI suggestions and unequal access to technology.

A small pilot can establish feasibility and generate preliminary evidence, but cannot by itself prove broad educational effectiveness.

7. Suggested Development Roadmap

Phase 1 — No-Code Prototype

Build the Reverse Teaching Assistant as a Custom GPT. Test it with three to five topics and teacher-approved materials.

Phase 2 — Working Web Application

Develop a Streamlit application using Python, the OpenAI API and SQLite. Implement lesson generation, rehearsal and teacher review.

Phase 3 — Quality and Safety Testing

Test factual accuracy, age appropriateness, accessibility, privacy protections and the reliability of generated questions.

Phase 4 — Classroom Pilot

Conduct supervised reverse-teaching sessions with consenting participants and record outcomes.

Phase 5 — Evaluation and Improvement

Analyze assessment results, student feedback and teacher observations. Improve prompts, workflows and instructional materials.

Phase 6 — Extended Implementation

If the pilot supports further development, consider multilingual explanations, subject-specific templates, LMS integration and accessible learning formats.

8. Expected Project Deliverables

Working AI Reverse Teaching Assistant.
Reusable lesson-planning and rehearsal prompts.
Student-facing web prototype.
Teacher review and approval interface.
Sample AI-supported teaching materials.
Assessment rubrics and evaluation dataset.
Classroom pilot report.
Implementation guide for teachers and institutions.

9. What Makes This an AI Creativity Project?

Ordinary educational AI often focuses on explaining lessons, generating notes or answering student questions.

This project begins with a deliberate lateral-thinking reversal:

What if students taught their teachers?

The movement reveals a different educational design: students learn through explanation, teachers guide the learning process, and AI supports preparation, rehearsal and feedback.

The innovation is not the AI-generated lesson plan. It is the redesigned learning relationship.

Lateral Thinking → AI Exploration → Prototype → Evaluation → Accomplishment

PO Prompts — Think the Unthinkable.

AIYING — Accomplishing with AI.

AI Innovation Project #001 — Building an AI Question Discovery Assistant | PO Prompts

2026-10-29T22:26:25.018+05:30

🚀 AI Innovation Project #001 | Question Discovery Assistant

What if AI stopped giving answers?

This provocation from PO Prompts #001 opens an interesting possibility:

An AI assistant that helps us discover better questions before rushing to answers.

The proposed project:
Build an AI-powered Question Discovery Assistant that:

Challenges hidden assumptions
Identifies missing evidence
Reveals overlooked perspectives
Suggests questions and experiments
Helps improve decision-making

The experiment: Compare decisions made using conventional AI answers with those supported by the Question Discovery Assistant.

Inspired by Edward de Bono's Serious Creativity, the project demonstrates how lateral thinking can take us beyond mere AI-generated ideas.

Lateral thinking discovers new directions. AI helps turn them into accomplishments.

🎬 Watch the 15-second concept video:
https://youtube.com/shorts/41NMZBbng-E

💬 Would you use an AI assistant that asks better questions instead of immediately giving answers?

PO Prompts — Think the Unthinkable.

AIYING — Accomplishing with AI

#POPrompts #LateralThinking #AIInnovation #GenerativeAI #AIYING

Technical Blueprint: Building the AI Question Discovery Assistant

Project objective: Develop an AI assistant that improves human reasoning by asking insightful questions, challenging assumptions and identifying missing evidence before recommending solutions.

1. Recommended Technology Stack

Component	Recommended tool	Purpose
AI model	OpenAI GPT models	Generate insightful questions, examine assumptions and analyze responses
Prototyping	ChatGPT Custom GPT	Build an initial no-code prototype
Programming	Python	Implement the application's logic
User interface	Streamlit	Create an interactive web application
AI integration	OpenAI API	Connect the application to an AI model
Workflow management	LangGraph (optional)	Coordinate multi-step questioning and evaluation
Data storage	SQLite	Store questions, responses and experiment results
Evaluation	Python, pandas	Analyze question quality and decision outcomes
Deployment	Streamlit Community Cloud	Publish a prototype for testing

Recommended starting point: Build a Custom GPT first. Move to Python, Streamlit and the OpenAI API when greater control, structured logging or systematic evaluation becomes necessary.

2. Proposed System Architecture

User Problem → Assumption Discovery → Question Generation → Evidence Identification → Reflection → Decision Support

The assistant operates through five stages:

Understand: Identify the user's problem, objective and decision context.
Challenge: Surface hidden assumptions and alternative interpretations.
Question: Generate five relevant, non-obvious questions.
Investigate: Identify missing evidence and suggest practical ways to obtain it.
Support: Help the user develop and evaluate possible decisions without pretending uncertain information is established fact.

3. Prototype Prompt

Create a Custom GPT using the following instruction:

You are a Question Discovery Assistant inspired by lateral-thinking principles.
Your primary responsibility is not to provide immediate answers but to improve the user's understanding of the problem.
For each problem:
Identify the underlying objective.
Surface three potentially limiting assumptions.
Ask five insightful questions, including at least one that challenges the conventional framing.
Identify important missing evidence.
Suggest two practical investigations or experiments.
Provide recommendations only when requested, clearly separating evidence from assumptions.
Be constructive, concise and curious. Never invent supporting evidence.

4. Minimum Viable Product (MVP)

Build a Streamlit application with:

A text box for entering a problem or decision.
A button to discover assumptions and questions.
A panel displaying five AI-generated questions.
An option to answer the questions and refine the analysis.
An evidence checklist.
An exportable decision summary.

Example input: "Should our college introduce AI-based examinations?"

Instead of immediately recommending yes or no, the assistant might ask:

What educational outcome are examinations supposed to measure?
Which assessment practices are we assuming must remain unchanged?
What evidence would show that AI improves or weakens learning?
Could assessment become continuous rather than examination-based?
What small pilot could test an alternative fairly?

5. Evaluation Experiment

Compare two approaches using the same set of decision problems.

Group A: Conventional AI assistant providing direct recommendations.

Group B: Question Discovery Assistant encouraging structured exploration before recommendations.

Evaluate:

Relevance and originality of questions.
Identification of hidden assumptions.
Quality of supporting evidence.
Consideration of alternative solutions.
Quality of the final decision rationale.
Time and effort required.

Use independent reviewers and a predefined scoring rubric. Avoid assuming that asking more questions automatically produces better decisions.

6. Suggested Development Roadmap

Phase 1 — No-code prototype: Build and manually test a Custom GPT.

Phase 2 — Application prototype: Implement the workflow using Python, Streamlit and the OpenAI API.

Phase 3 — Evaluation: Test 10–20 representative decision scenarios, compare against a direct-answer baseline and review failure cases.

Phase 4 — Improvement: Refine questioning strategies, add evidence checks and strengthen privacy and safety controls.

Phase 5 — Pilot: Invite a small group of students, teachers or professionals to evaluate the application.

Expected Deliverables

Working Question Discovery Assistant.
Reusable prompting framework.
Interactive web prototype.
Evaluation rubric and test dataset.
Pilot report documenting strengths, limitations and improvement opportunities.

Beyond Mere Generation

The distinctive feature of this project is not simply generating more questions.

It is deliberately challenging existing thinking patterns, using lateral-thinking techniques to discover new directions, and employing AI to investigate those directions.

Lateral thinking is the creative engine. AI is the exploration amplifier. Accomplishment is the destination.

PO Prompts — Think the Unthinkable.

AIYING — Accomplishing with AI

What If AI Stopped Answering Us? 🤯 Something Amazing Happens! #Shorts

2026-10-29T21:41:56.720+05:30

AI Creativity #001 — What If AI Had No Answers? | Edward de Bono's Escape Provocation

2026-10-29T20:28:14.915+05:30

What If AI Had No Answers?

How Edward de Bono’s Escape Provocation Can Help Us Reinvent the Role of AI

AI Creativity — Discover New Possibilities. Accomplish with AI.

Imagine opening ChatGPT, asking a question, and receiving no answer.

Instead, AI asks you another question.

You ask it to solve a problem. It refuses to provide a ready-made solution.

You ask for a business idea. It asks you to identify an opportunity nobody else has noticed.

Would such an AI be useless?

Or could it help us discover something that conventional AI interactions often overlook?

Welcome to AI Creativity, a series exploring how the deliberate creative thinking methods developed by Edward de Bono can help us discover and accomplish genuinely new things in the age of generative AI.

In this first episode, we explore one of de Bono's lateral thinking techniques: Escape Provocation.

Our provocative starting point is simple:

PO: AI assistants do not give answers.

Let's see where this apparently unreasonable idea takes us.

1. Why Do We Expect AI to Have All the Answers?

Generative AI has transformed our relationship with information and problem-solving.

We can ask AI to explain difficult concepts, write documents, generate software, prepare presentations, analyze data, suggest business ideas, and help make decisions.

Naturally, much of our interaction with AI follows a familiar pattern:

Human asks → AI answers → Human uses the answer.

This pattern is powerful and often highly productive.

But it also raises an interesting question.

When AI provides an immediate answer, what happens to the thinking that might otherwise have taken place?

Consider three situations.

A student asks AI to solve a mathematics problem. The answer may be correct, but the student may never develop the reasoning needed to solve similar problems independently.

A manager asks AI to recommend a business decision. The recommendation may be useful, but the manager might overlook alternative ways of framing the problem.

An entrepreneur asks AI for startup ideas. AI produces a familiar list of possibilities, but the entrepreneur may never discover an entirely different kind of opportunity.

These examples do not mean that AI answers are inherently harmful. Direct answers are often exactly what people need.

The creative opportunity lies elsewhere.

What if we redesigned some AI interactions to develop human thinking rather than immediately replace it?

To explore this question, we turn to Edward de Bono.

2. Edward de Bono and the Escape Provocation Method

In his book Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas (1992), Edward de Bono explains that our thinking tends to follow established patterns.

These patterns are useful. They allow us to recognize situations, apply experience, and reach conclusions efficiently.

However, established patterns can also restrict our ability to see alternatives.

Lateral thinking provides deliberate methods for moving beyond familiar patterns.

One such method is Escape Provocation.

How escape provocation works

The process begins by identifying something we normally take for granted.

We then deliberately remove, cancel, or negate that feature.

For example, de Bono considers the familiar assumption that restaurants have food.

He introduces the provocation:

PO: Restaurants do not have food.

At first, this appears absurd.

But instead of rejecting the idea, he explores what might happen if people visited an attractive restaurant without food being provided.

Perhaps visitors would bring their own food.

Perhaps the restaurant would provide the setting, atmosphere, seating, and service rather than the meal.

This leads toward the concept of an elegant indoor picnic venue.

The important point is that the provocation is not a proposal to eliminate food from restaurants.

It is a device for discovering a different business concept.

De Bono calls the deliberate exploration that follows a provocation movement.

A provocation without movement may remain merely strange. Movement is what helps transform it into potentially useful ideas.

A crucial distinction

Escape provocation is not simply brainstorming impossible ideas.

It has a specific structure:

Identify something normally taken for granted.
Deliberately escape from that feature.
Use movement to explore consequences and possibilities.
Develop useful concepts emerging from that exploration.

De Bono also cautions against selecting a problem or complaint as the feature to remove. The starting point should be an ordinary, established feature of the situation.

Now let's apply the method to AI.

3. Our AI-Age Provocation: AI Has No Answers

We begin with a feature of AI assistants that most people take for granted:

AI assistants provide answers to users' questions.

We deliberately remove that feature.

PO: AI assistants do not give answers.

This is not a prediction about the future of AI.

It is not a recommendation that AI systems should stop answering questions.

It is a creative provocation designed to interrupt our usual way of thinking.

The immediate reaction might be:

What is the point of an assistant that cannot answer?

But lateral thinking asks us to resist dismissing the provocation.

Instead, we explore it.

If an AI assistant could not provide answers, what else could it do?

It might ask questions.

It might reveal assumptions.

It might suggest experiments.

It might show competing viewpoints.

It might help people discover their own solutions.

Suddenly, a new possibility emerges.

AI need not always be an answer machine. It could also be a discovery partner.

That is our first movement away from the conventional concept.

4. Moving from an Impossible Idea to New Possibilities

Let's explore several directions opened by the provocation.

Possibility 1: The AI Discovery Tutor

Imagine a student asking:

Why does a metal spoon feel colder than a wooden spoon?

A conventional AI assistant might immediately explain that metal conducts heat more effectively than wood, causing heat to leave the student's hand more quickly.

That is a useful explanation.

But imagine an AI tutor designed to help the student discover the principle.

Student: Why does a metal spoon feel colder than a wooden spoon?

AI: Do you think the two spoons are actually at different temperatures?

Student: Maybe.

AI: How could we find out?

Student: We could measure their temperatures.

AI: Suppose they are at nearly the same temperature. Why might they feel different?

Student: Maybe one transfers heat faster.

AI: Excellent. How could you test that idea?

The student is no longer simply receiving information.

The student is participating in the construction of an explanation.

This suggests an AI learning system that can deliberately guide inquiry, encourage experimentation, and help learners develop understanding.

The idea is not that AI must withhold answers indefinitely. A good tutor would provide explanations when appropriate, correct misconceptions, and adapt to the learner's needs.

Creative outcome: An AI assistant designed to cultivate independent reasoning.

Possibility 2: The AI Decision Explorer

A manager asks:

Should our company introduce a four-day workweek?

A conventional AI assistant might provide advantages, disadvantages, and a recommendation.

But our provocation encourages a different approach.

Instead of immediately recommending a decision, AI could help the manager examine the decision itself.

It might ask:

What outcome are you actually trying to improve?
Why is the number of working days the central issue?
Could the same objective be achieved through a different work arrangement?
Which assumptions about productivity have not been tested?
What small experiment could provide useful evidence?

The manager may discover that the real problem is not the length of the workweek.

It could be meeting overload, fragmented attention, scheduling, or inefficient coordination.

The original question may therefore lead to a different and potentially more useful problem definition.

Creative outcome: An AI decision explorer that helps organizations discover better questions before selecting solutions.

Possibility 3: The AI Opportunity Discoverer

An entrepreneur asks:

Give me ten business ideas using AI.

An answer-oriented assistant can generate a list quickly.

But many of those ideas may resemble existing products and services.

What if the AI assistant were designed to discover opportunities before proposing solutions?

It might ask the entrepreneur:

Which activities do people repeatedly perform even though they dislike doing them?

Where do customers accept inconvenience because they believe no alternative exists?

What do organizations pay for that customers do not actually value?

Which services have remained structurally unchanged despite technological advances?

What would happen if one supposedly essential feature disappeared?

The entrepreneur begins examining unmet needs, hidden assumptions, and neglected opportunities.

The resulting ideas might be more closely connected to real problems.

Creative outcome: An AI opportunity-discovery system that begins with overlooked needs rather than ready-made startup suggestions.

Possibility 4: The AI Thinking Coach

A professional asks:

Write my project proposal.

Instead of immediately producing a polished document, AI might first help clarify the underlying thinking.

What is the intended outcome?

What problem does the proposal solve?

What evidence supports the recommendation?

What alternatives have been considered?

What would make the proposal distinctive?

Only after these questions have been explored does AI help create the document.

The result could be more than a well-written proposal.

It could be a better-conceived proposal.

Creative outcome: An AI writing partner that strengthens the quality of ideas before improving their presentation.

5. A New Concept Emerges: The AI Discovery Partner

Our original provocation was:

PO: AI assistants do not give answers.

Through movement, we have discovered several possible directions.

These directions share an underlying concept.

Instead of treating every interaction as a request for information, we can treat some interactions as opportunities to develop human thinking.

This leads to a proposed AI service concept:

The AI Discovery Partner

An AI assistant that deliberately chooses among different ways of helping the user.

Answer Mode

Provides direct answers, explanations, instructions, and practical assistance when that is what the user needs.

Discover Mode

Uses questions, hints, examples, and experiments to help the user develop understanding independently.

Challenge Mode

Tests assumptions, introduces alternative perspectives, and encourages deeper examination of ideas.

Create Mode

Uses deliberate creativity techniques to help users explore unconventional concepts and develop new possibilities.

The innovation opportunity is not simply adding four labels to a chatbot.

It lies in designing an interaction system that recognizes the user's purpose and helps select the most appropriate form of assistance.

For example, someone seeking urgent safety information needs clear and immediate guidance, not a prolonged series of questions.

Someone learning a scientific concept might benefit from guided discovery.

Someone developing an innovative product might benefit from provocations and challenges.

The right kind of AI assistance depends on the kind of human thinking we want to support.

This is a promising design direction, not a claim that such capabilities are entirely absent from existing AI systems.

6. Let's Use AI to Apply Escape Provocation

Now comes the practical part of the episode.

You can use the same technique with your own challenges.

Here is a reusable prompt.

AI Creativity Prompt #001 — Escape Provocation

Copy and use this prompt in your preferred AI assistant:

Act as my creative thinking partner. Help me apply Edward de Bono's Escape Provocation technique to discover genuinely different possibilities.
My focus: [Describe the activity, problem, product, service, or process I want to rethink.]
Step 1 — Identify: List five ordinary features that people take for granted in this situation. Do not select problems or complaints.
Step 2 — Escape: Select one feature and deliberately remove or negate it. Express the result as a PO provocation.
Step 3 — Move: Do not immediately reject the provocation or simply restore the missing feature. Explore what might happen, what changes, and what unexpected possibilities emerge.
Step 4 — Generate: Develop five different concepts inspired by the movement. Explain how each differs from conventional approaches.
Step 5 — Judge: Evaluate the concepts for potential novelty, usefulness, feasibility, and risks. Identify what needs verification.
Step 6 — Accomplish: Select one promising concept and design a small practical experiment I can complete within a week.
Guide me through the process interactively. Help me discover possibilities I would be unlikely to consider through ordinary brainstorming.

This prompt combines de Bono's original technique with a contemporary AI-assisted exploration and evaluation workflow.

The creative method provides the direction.

AI helps expand, examine, and develop the possibilities.

Human judgment remains essential.

7. Another Example: What If Job Applications Had No Résumés?

Let's apply the method to a different contemporary challenge.

Reality

Job applications commonly involve submitting a résumé or CV.

Employers use these documents to understand a candidate's education, experience, and skills.

Take-for-granted feature

Job applicants submit résumés.

Escape provocation

PO: Job applications have no résumés.

What happens next?

An employer still needs to understand a candidate's capabilities.

A candidate still needs to demonstrate suitability.

But without a résumé, both parties must find other ways to communicate relevant evidence.

Movement

Perhaps candidates demonstrate their abilities through small, realistic work samples.

Perhaps AI helps employers create fair, role-relevant tasks.

Perhaps applicants build evidence portfolios rather than merely describing their skills.

Perhaps candidates and employers begin with a structured conversation about actual work requirements.

Emerging concept

An AI-assisted capability demonstration platform.

Instead of relying primarily on claims in a résumé, applicants could demonstrate selected skills through short, relevant exercises.

AI might assist with task preparation, accessibility, feedback, and evidence organization, while employers retain responsibility for fair assessment and hiring decisions.

This concept would require careful consideration of privacy, bias, accessibility, unpaid work, and the validity of assessments.

It is also important to recognize that skills-based hiring and work-sample assessments already exist. Our provocation does not establish that the concept is globally new.

Its value is in helping us discover and refine a different direction.

That is what serious creativity is about.

8. How Do We Judge Whether an Idea Is Worth Pursuing?

A provocative idea is not automatically a good idea.

Neither is an AI-generated suggestion.

After generating possibilities, we need to evaluate them.

For our AI Discovery Partner concept, we can ask five questions.

Criterion	Question
Novelty	What is genuinely different from existing approaches?
Usefulness	Whose problem does the idea solve?
Feasibility	Can we build or test a simple version?
Human value	Does it improve understanding, autonomy, or decision quality?
Responsibility	What risks, limitations, or unintended effects must be addressed?

We should also avoid confusing novelty of presentation with novelty of substance.

An AI system that asks questions is not automatically innovative.

A more meaningful innovation might lie in how it selects questions, adapts to the learner, measures understanding, or integrates discovery into real work.

That is where further experimentation becomes valuable.

9. Accomplish Something: Your 10-Minute AI Creativity Challenge

You do not need to be a professional innovator to practice lateral thinking.

Try this simple exercise today.

Minute 1–2: Choose a familiar activity.

It could be studying, attending meetings, teaching, shopping, searching for work, or managing daily tasks.

Minute 3–4: Identify something taken for granted.

For example:

Meetings have agendas.

Schools have classrooms.

Shopping involves choosing products.

AI assistants answer questions.

Minute 5: Remove it.

Create a PO provocation.

PO: Meetings have no agendas.

Minute 6–8: Explore with AI.

Use the Escape Provocation prompt above to generate alternative concepts.

Minute 9: Judge.

Select one idea that appears both interesting and useful.

Minute 10: Accomplish.

Define one small action that would test the idea.

The aim is not to invent a revolutionary product in ten minutes.

The aim is to experience a deliberate change in your thinking.

Repeat the exercise regularly, and you begin developing a practical creative habit.

10. What Does This Mean for Creativity in the AI Age?

Generative AI has made producing possibilities faster and more accessible.

But producing many ideas is not the same as escaping established thinking patterns.

If we repeatedly ask AI familiar questions, we may repeatedly explore familiar directions.

De Bono's techniques offer another starting point.

They help us deliberately disturb the assumptions that shape our questions.

AI can then become a powerful collaborator in exploring the resulting possibilities.

This suggests a useful division of creative work:

Human creative intention identifies where to challenge established thinking.

Lateral thinking techniques provide deliberate ways to escape familiar patterns.

Generative AI helps explore, elaborate, compare, and prototype possible directions.

Human judgment determines what is valuable, responsible, and worth pursuing.

Practical experimentation turns a promising possibility into something we can learn from.

The important change is not simply that AI can generate ideas.

It is that people can combine deliberate creative thinking with AI to explore possibilities that might otherwise remain unnoticed.

11. The Takeaway: AI Should Expand Thinking, Not Merely Accelerate Answers

We began with an apparently unreasonable statement:

PO: AI assistants do not give answers.

We did not conclude that AI should stop answering questions.

Instead, we discovered a different design possibility:

AI can be developed not only to provide answers, but also to strengthen the human capacity to discover them.

That is the value of escape provocation.

By deliberately removing something familiar, we open a path toward something different.

And by combining lateral thinking with generative AI, we can explore that path more extensively.

Your turn

What is one thing you take for granted about AI today?

Remove it imaginatively.

What new possibility appears?

Share your PO provocation and the idea it helped you discover.

The most interesting possibilities may begin with the assumptions we have never thought to question.

Coming Next: AI Creativity #002

Creative Pause — What Are We Failing to Notice in the AI Age?

In the next episode, we will explore another technique from de Bono's Serious Creativity: the Creative Pause.

Can deliberately interrupting our normal flow of thinking help us discover opportunities that constant activity and rapid AI responses conceal?

We'll investigate through practical examples and an AI-assisted creativity experiment.

About the AI Creativity Series

AI Creativity systematically revisits creative thinking methods described by Edward de Bono and explores how they can be applied to contemporary challenges using generative AI.

Each episode introduces one technique, applies it to a relevant situation, develops new possibilities, evaluates them, and provides a practical activity or reusable AI prompt.

The goal is not merely to generate more ideas. It is to discover better directions and accomplish something genuinely useful.

Reference: Edward de Bono, Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas (1992), particularly the chapters on Provocation, Movement, and Setting Up Provocations. Contemporary AI applications, prompts, and examples are original adaptations for this series.

AI Creativity — Discover New Possibilities. Accomplish with AI.

AIYING — Accomplishing with AI.

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

2026-10-29T18:20:42.399+05:30

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?

2026-10-29T17:20:59.969+05:30

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 #005 — What If Yesterday's Success Stories Were Forbidden? | AI Careers and New Possibilities

2026-10-29T13:04:50.925+05:30

What If Yesterday's Success Stories Were Forbidden?

One Strange Question. A New Possibility.

An Indian MBA campus story, Sam Altman's challenge to conventional ambition, and a practical PO–AI Thinking Lab for discovering opportunities that don't yet have a name.

The Question That Started It All

Every generation grows up admiring the success stories of the generation before it.

Students study successful companies.

Business schools teach established management practices.

Young professionals seek prestigious careers.

Entrepreneurs examine proven business models.

There is considerable value in learning from experience.

But what happens when the world changes faster than our definitions of success?

What if the career everyone wants today was designed for yesterday's economy?

What if the next extraordinary business opportunity does not resemble any company we currently admire?

And what if AI could help us discover possibilities beyond established career and business models?

These questions become particularly interesting in light of a recent observation by Sam Altman.

The News That Inspired This Episode

In an October 29, 2026, Times of India report, Sam Altman was quoted criticizing the tendency of MBA students to pursue opportunities that had been fashionable years earlier.

Speaking in an interview with Stanford Graduate School of Business, according to the report, Altman encouraged ambitious people to explore new opportunities emerging from technological change.

His central concern was not that established careers have no value. It was that aspiring leaders may overlook emerging opportunities when they focus too heavily on familiar success stories.

The report also described Altman's view that AI could enable new companies and significantly change how work is performed.

Source: The Times of India — Sam Altman on MBA students and new opportunities

This reported observation offers a powerful starting point for lateral thinking.

But rather than merely agreeing or disagreeing with Altman, let's introduce a deliberate provocation.

What if yesterday's success stories were forbidden?

1. The Story: Aditi and the Placement Race

It was placement season at a prestigious Indian business school.

The campus was buzzing with excitement.

Students were preparing for interviews with consulting firms, investment banks, multinational corporations and established technology companies.

There were workshops on résumé building, mock interviews, aptitude tests and case-study preparation.

Everyone seemed to know what success looked like.

A prestigious employer.

An attractive salary.

An impressive job title.

A career path that others would recognize and admire.

Aditi, a final-year MBA student, was among the most promising candidates in her batch.

She had excellent academic results, strong communication skills and several internship experiences.

Yet she felt strangely uncertain.

The familiar question

One evening, Aditi opened ChatGPT.

She typed:

"What are the best career opportunities for an MBA graduate in the AI era?"

The response was impressive.

It presented career options, skill requirements, industry trends and recommended learning paths.

Consulting.

Product management.

Corporate strategy.

Business analytics.

Technology leadership.

Entrepreneurship.

The answer was sensible.

But Aditi noticed something.

Most of the recommendations resembled paths that previous generations of successful graduates had already followed.

She wondered:

If AI is changing the world so dramatically, why am I still planning my future by looking backward?

The professor's unusual assignment

The next morning, during an innovation class, Professor Rao introduced a challenge.

“Imagine,” he said, “that every successful career path and business model from the past five years has become unavailable.”

The students laughed.

“That would eliminate most of our placement choices,” someone replied.

“Exactly,” said the professor.

He wrote a statement on the board:

PO: You are forbidden to copy yesterday's successful careers or business models.

Aditi raised her hand.

“Then how do we know what to pursue?”

Professor Rao replied:

“Perhaps that is the question you should be asking.”

The classroom fell silent.

For the first time, the students were not being asked to choose among established opportunities.

They were being challenged to imagine opportunities that might not yet exist.

The discovery

Aditi returned to ChatGPT.

But this time, she did not ask for the best jobs.

She asked:

"What important problems are emerging because AI is changing how people live and work? Which of these problems might require entirely new professional roles, services or organizations?"

The conversation changed.

Instead of merely ranking careers, AI began helping her explore emerging needs.

Who would help small businesses evaluate AI-generated decisions?

Who would design trustworthy human–AI workflows?

Who would help local organizations adapt AI tools to their languages, customers and working practices?

Who would investigate failures when autonomous systems made mistakes?

Who would help professionals preserve accountability while delegating routine work to AI?

Aditi had not discovered a guaranteed career.

She had discovered something potentially more valuable.

A new way of searching for opportunities.

And it began with a strange question.

The characters, institution and classroom events in this story are fictional. They illustrate the PO thinking method and are not presented as a real incident.

2. The Hidden Assumption: Success Means Following a Successful Path

Many students and professionals make career decisions by studying people who have already succeeded.

This is understandable.

Successful examples provide evidence, inspiration and practical guidance.

But they can also influence what we consider possible.

Consider some familiar assumptions.

Assumption 1: Prestigious careers are necessarily the best careers.

A recognized job title may provide status, income and opportunity. But it does not automatically guarantee suitability, satisfaction or long-term resilience.

Assumption 2: Established industries offer the most attractive opportunities.

Mature industries may provide excellent careers. Yet emerging needs can also create opportunities that are not reflected in conventional placement lists.

Assumption 3: The most popular career choice is probably the safest.

Popularity does not establish future security. Economic conditions, technological capabilities and employer needs can change.

Assumption 4: A new opportunity must resemble an existing job.

Sometimes new technologies create combinations of responsibilities that do not fit established professional categories.

Assumption 5: AI should tell us which career to choose.

AI can organize information and compare options. But its more interesting contribution may be helping us discover possibilities we have not considered.

The challenge is not to reject existing career paths.

It is to avoid treating them as the complete universe of opportunity.

A better question

Instead of asking:

“Which successful career should I follow?”

Ask:

“What important problems are emerging, and what new capabilities might be needed to solve them?”

That shift prepares us for PO.

3. Understanding PO: The Escape Technique

Edward de Bono developed lateral-thinking techniques to help people move beyond established patterns.

One such technique is escape.

In escape provocation, we identify something normally taken for granted and temporarily remove it.

The purpose is not to argue that the assumption should disappear in reality.

The purpose is to discover what becomes visible when we imagine its absence.

For this episode, the assumption is:

Established success stories provide the natural starting point for future career and business decisions.

Now remove that assumption.

The PO provocation

PO: Yesterday's successful career paths and business models cannot be copied.

Imagine that for one week:

You cannot choose a career simply because it is prestigious.

You cannot pursue a business idea merely because another startup has succeeded with it.

You cannot use an established job title as your starting point.

You must begin with emerging problems, changing needs and unexplored possibilities.

What would you investigate?

Who would you talk to?

What would you ask AI?

Which skills would suddenly become relevant?

This is where deliberate movement begins.

The provocation is not a literal career restriction.

It is a temporary thinking experiment.

4. The PO–AI Thinking Lab

Our refined PO–AI Thinking Lab follows six stages:

FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH

The first stages draw on Edward de Bono's methods of provocation and movement. The integration with generative AI, critical evaluation and practical experimentation is the AIYING extension.

Let's apply the framework to Aditi's challenge.

Stage 1 — FOCUS: What Are We Really Trying to Achieve?

Aditi's initial question was:

"Which MBA career should I choose?"

But that question already assumes that the correct opportunity can be found among recognizable career categories.

A more fundamental question is:

“How can I build a meaningful, economically viable and adaptable professional future in a rapidly changing world?”

This is a broader and potentially more useful focus.

Questions for reflection

What does career success mean to me?

Is it primarily income, security, independence, learning, social contribution or some combination?

Which abilities do I genuinely enjoy developing?

What kinds of problems motivate me?

What constraints must I respect?

What opportunities might I overlook because they do not have familiar names?

Aditi's initial assumptions

She identifies four assumptions:

Her MBA qualification should lead to a conventional management role.
A prestigious employer is the best evidence of a good career decision.
New opportunities are too uncertain to investigate seriously.
AI is primarily useful for comparing existing career choices.

She does not need to reject these assumptions permanently.

She needs to examine them.

FOCUS outcome: Redefine the objective from selecting a prestigious job to discovering a sustainable professional direction.

Stage 2 — PROVOKE: Remove the Familiar Paths

Now Aditi introduces the escape provocation:

PO: I am forbidden to copy any successful career path or business model from the previous five years.

Imagine that conventional consulting, banking, product-management and startup models are unavailable as templates.

This does not mean that the underlying skills become useless.

Analytical thinking, financial literacy, communication, operations and leadership remain valuable.

But Aditi cannot simply imitate the familiar professional destination.

She must search for something different.

The immediate discomfort

How will she explain her choice to recruiters?

How will she estimate income?

How will she know whether an opportunity is real?

What skills should she acquire?

These concerns are legitimate.

But during the provocation stage, she temporarily postpones judgment.

The goal is to expand the space of possibilities before evaluating them.

PROVOKE outcome: Remove dependence on familiar success models as the starting point.

Stage 3 — MOVE: Discover What the Provocation Reveals

Provocation alone is not enough.

We must move from the unusual statement toward useful concepts.

Let's apply several movement approaches.

Movement A — Extract the principle

What principle might emerge from the prohibition on copying old success models?

Build your future around emerging problems, not merely established job titles.

This principle can guide practical exploration without requiring us to abandon conventional careers.

Movement B — Examine the positive aspects

What might be useful about temporarily ignoring familiar career templates?

Aditi may notice opportunities that placement rankings overlook.

She may explore combinations of business and technical capabilities.

She may discover organizations that need new kinds of services.

She may identify problems that established companies have not prioritized.

She may become more willing to test ideas before committing to a career direction.

Movement C — Explore consequences

If graduates stopped relying exclusively on familiar career categories, what might happen?

Some would begin studying emerging customer and organizational problems.

Others would explore new combinations of existing skills.

Some might discover opportunities within established companies rather than outside them.

Universities might introduce more problem-discovery projects alongside conventional placement preparation.

Employers might recruit for capabilities and outcomes rather than only established titles.

These are possibilities, not predictions.

Movement D — Identify differences

Compare two ways of exploring a career.

Career-first approach:

Choose a profession → Study its requirements → Acquire qualifications → Apply for roles.

Problem-first approach:

Identify an important problem → Understand who experiences it → Investigate possible solutions → Determine which capabilities are needed → Test a professional opportunity.

The two approaches can complement each other.

But the second may reveal directions that the first overlooks.

Movement E — Find useful circumstances

Where could this approach be especially valuable?

During technological transitions.

When industries face unfamiliar regulatory or operational challenges.

When organizations adopt new AI capabilities.

When local communities have unmet needs.

When professional roles combine disciplines that have traditionally been separate.

The movement insight

Aditi begins with an impossible restriction.

She arrives at a practical principle:

Don't merely search for a job title. Search for a valuable problem that your capabilities could help solve.

MOVE outcome: Shift from career imitation toward problem-led opportunity discovery.

5. Stage 4 — GENERATE: Five New Possibility Directions with AI

Now Aditi uses generative AI to expand the directions uncovered by PO.

The following examples are exploratory concepts, not verified job openings or forecasts.

Possibility 1 — Human–AI Workflow Designer

As organizations introduce AI into everyday work, they must decide which activities can be automated and which require human judgment.

A possible professional opportunity is helping organizations design workflows that combine human expertise with AI capabilities.

For example, a small business might use AI to prepare customer communications, but retain human approval for sensitive decisions.

A workflow designer could help identify tasks, responsibilities, controls and measures of success.

Skills to investigate: Process mapping, business analysis, AI literacy, change management and evaluation.

Question to test: Are organizations willing to pay for help redesigning specific workflows around AI?

Possibility 2 — AI Decision Accountability Specialist

AI-assisted decisions can create difficult questions.

Who checks the output?

Who approves consequential actions?

What evidence supports a recommendation?

How are errors detected and corrected?

Organizations may need people who understand both business decisions and AI governance.

Aditi could investigate opportunities related to decision oversight, documentation, escalation processes and accountability.

Skills to investigate: Risk management, responsible AI, governance, auditing and organizational processes.

Question to test: Which organizations have unresolved accountability problems in their current AI workflows?

Possibility 3 — Local-Language AI Service Innovator

India has enormous linguistic, cultural and organizational diversity.

A service that works well in one language or context may be difficult to use in another.

Aditi could explore AI-assisted services designed around specific local needs.

Examples might include supporting small businesses with multilingual customer communication or helping service organizations make information easier to understand.

Skills to investigate: Customer research, multilingual service design, usability testing and AI tool evaluation.

Question to test: What important communication difficulties remain unresolved in a specific local market?

Possibility 4 — AI Adoption Experiment Designer

Many organizations are interested in AI but uncertain about where it will produce meaningful benefits.

Instead of offering general AI training, a professional might help organizations design small, measurable adoption experiments.

For example:

Can an AI-assisted process reduce the time required to prepare routine reports without increasing errors?

Can a customer-service team use AI to improve response consistency while maintaining human oversight?

Skills to investigate: Experiment design, operational metrics, project management and process improvement.

Question to test: Would organizations value structured experiments more than generic AI demonstrations?

Possibility 5 — Emerging-Problem Discovery Consultant

Organizations often begin innovation by asking for new solutions.

But what if the more valuable service is helping them discover which problems deserve attention?

Aditi could explore a consulting model that combines customer interviews, PO provocations, AI-assisted analysis and small experiments.

The consultant would not begin with a technology recommendation.

The process would begin with the organization's unresolved difficulties.

Skills to investigate: Research methods, facilitation, lateral thinking, business modeling and validation.

Question to test: Would a client pay for a clearer problem definition before investing in a solution?

6. Five More Possibilities Worth Exploring

The first five possibilities are only starting points.

Aditi could ask AI to explore additional combinations:

AI Evaluation Coordinator: Helping organizations test whether AI systems perform reliably on real tasks.

Human–Agent Operations Planner: Designing responsibilities and oversight when AI agents participate in operational workflows.

AI Accessibility Service Designer: Exploring ways to make digital services more accessible to people with different needs.

Workforce Transition Facilitator: Helping teams identify how changing technology affects tasks, capabilities and learning requirements.

Community AI Opportunity Researcher: Investigating locally important problems that might benefit from appropriate AI-enabled services.

Again, these are conceptual directions. Their economic viability, market demand and appropriate qualifications must be independently established.

A critical distinction

AI can generate plausible descriptions of new opportunities.

It cannot establish that employers are hiring for them or that customers will pay for them merely by describing them confidently.

An AI-generated opportunity is a hypothesis until evidence supports it.

7. Stage 5 — JUDGE: Which Possibilities Deserve Serious Attention?

This is where enthusiasm meets evidence.

Aditi should not abandon a credible job opportunity because an AI-generated career concept sounds exciting.

She needs a disciplined evaluation process.

Consider five criteria.

1. Meaningful demand

Who experiences the problem?

How frequently?

How costly or consequential is it?

What are they doing about it today?

2. Personal fit

Does the opportunity match Aditi's strengths and interests?

Can she acquire the necessary capabilities?

Would she enjoy the daily work?

3. Feasibility

Can she enter the field without unreasonable financial or professional risk?

Are there accessible organizations, customers or projects where she can gain experience?

4. Evidence

Are there real job descriptions, paid projects, customer interviews or documented organizational needs supporting the opportunity?

What remains speculation?

5. Responsible implementation

Does the opportunity involve sensitive information, high-stakes decisions or regulatory obligations?

What expertise and safeguards would be required?

Illustrative evaluation matrix

Possibility	Main attraction	Key uncertainty	First evidence to collect
Human–AI Workflow Designer	Combines business and AI skills	Will organizations fund redesign work?	Interviews with operations managers
AI Decision Accountability Specialist	Addresses governance needs	Required expertise and qualifications	Real role descriptions and practitioner interviews
Local-Language AI Service Innovator	Addresses context-specific needs	Will users adopt and pay?	Customer interviews and prototype testing
AI Adoption Experiment Designer	Produces measurable business outcomes	Is there demand for facilitated pilots?	Discussions with small-business owners
Emerging-Problem Discovery Consultant	Differentiated innovation approach	Will clients pay for discovery?	One small paid or pilot engagement

The matrix is a framework for investigation, not a ranking based on market research.

Aditi's decision

She decides not to choose a new career immediately.

Instead, she selects two possibilities for further investigation.

She will compare them with her conventional career options using real evidence.

This preserves both creativity and prudence.

JUDGE outcome: Select promising opportunities for testing without confusing novelty with viability.

8. Stage 6 — ACCOMPLISH: The Seven-Day Opportunity Discovery Challenge

The objective is simple:

Discover and test one promising opportunity that you would not have considered through conventional career planning alone.

This exercise is useful for students, professionals considering a transition, educators and aspiring entrepreneurs.

Day 1 — Map Your Conventional Choices

List your five most obvious career or business options.

For each, record why it appeals to you.

Is it compensation?

Prestige?

Security?

Interest?

Family expectations?

A successful role model?

Then identify which assumptions shape your choices.

Day 2 — Apply the PO Escape

Temporarily remove your five familiar options.

Ask:

“If none of these paths existed, what important problems would I want to help solve?”

Do not evaluate the ideas yet.

Use AI to expand your thinking.

Day 3 — Discover Emerging Problems

Investigate problems in three domains that interest you.

Look for credible evidence of changing needs.

Speak with practitioners.

Read relevant industry material.

Observe workflows.

Use AI to organize findings and propose questions, but verify factual claims independently.

Day 4 — Generate Alternative Opportunities

Ask AI to develop ten different professional or entrepreneurial responses to the problems you identified.

Avoid merely renaming familiar jobs.

Explore different delivery models, customers, combinations of skills and forms of value.

Day 5 — Evaluate the Evidence

Shortlist three possibilities.

For each, identify:

Who needs it
Why it matters
What capabilities are required
What evidence exists
What risks are involved
What remains uncertain

Do not confuse AI-generated explanations with market validation.

Day 6 — Test One Possibility

Conduct a small experiment.

Possible experiments include interviewing a potential customer, discussing the concept with a professional, developing a simple demonstration or offering to solve one narrowly defined problem.

The experiment should be inexpensive, ethical and reversible.

Day 7 — Decide What to Learn Next

Review what you discovered.

Did you identify a meaningful problem?

Did someone confirm that the problem matters?

Could you contribute value?

What skills would you need?

What evidence would justify investing more time?

You may decide to pursue the opportunity, investigate further or abandon it.

All three can be useful outcomes.

The real measure of success

The challenge is not to invent a fashionable new job title.

It is to discover an opportunity that is:

Meaningful enough to matter. Plausible enough to investigate. Valuable enough to test.

9. The Complete ChatGPT PO Prompt

Here is the full reusable prompt for students, professionals and entrepreneurs.

Copy this prompt into ChatGPT

Act as my PO lateral-thinking partner, using Edward de Bono's provocation and movement methods.

My challenge is to discover new career, business or entrepreneurial possibilities in the AI era.

Use this provocation:

“PO: I am forbidden to copy any successful career path or business model from the past five years.”

Guide me through the following six stages.

1. FOCUS

Help me identify the conventional paths I am considering and the hidden assumptions behind them.

Explore what success means to me, my existing capabilities, interests, constraints and aspirations.

2. PROVOKE

Apply the escape technique.

Temporarily remove familiar success models and conventional job titles from consideration.

Do not dismiss the provocation merely because it is unrealistic.

3. MOVE

Use principle extraction, positive aspects, consequences, differences and special circumstances to discover useful directions emerging from the provocation.

Help me identify what changes when I begin with emerging problems rather than established career paths.

4. GENERATE

Develop at least ten genuinely different career, professional-service or entrepreneurial possibilities.

Base them on plausible emerging needs, changing technologies, organizational challenges or human problems.

Avoid simply producing a list of fashionable AI jobs.

For each possibility, explain the problem addressed, who might benefit, what capabilities are needed and what makes the direction distinctive.

5. JUDGE

Help me evaluate the most promising possibilities using:

Potential demand
Personal fit
Required capabilities
Feasibility
Risks
Available evidence
Uncertainties

Clearly distinguish verified facts from assumptions, speculative ideas and AI-generated hypotheses.

Do not invent job openings, market statistics, customer demand or evidence.

Where current information is necessary, identify what must be researched and verified.

6. ACCOMPLISH

Help me select one promising possibility and design a seven-day, low-cost experiment.

Include:

The hypothesis to test
People or organizations to approach
Questions to ask
A small practical activity
Evidence to collect
Criteria for deciding the next step

Ask me questions at each stage rather than immediately producing a final recommendation.

Begin by asking about my background, skills, interests, ambitions and current career or business options.

10. How to Make This Prompt More Useful

The quality of the exploration improves when you provide real context.

For example:

For an MBA student

“I am completing my MBA in marketing and operations. I enjoy solving customer problems and have experience in retail. Help me discover unconventional opportunities related to AI and changing consumer behavior.”

For an engineering graduate

“I have studied computer science and enjoy building software, but I don't want to assume that conventional software-development roles are my only options. Help me explore emerging problems I could address.”

For an experienced professional

“I have fifteen years of experience in operations management. I want to investigate how my expertise might create value in organizations adopting AI.”

For an entrepreneur

“I operate a small service business. Help me identify new customer problems and business models that might emerge as AI changes expectations and workflows.”

The prompt should adapt to the person's actual situation.

A student with limited resources needs a different experiment from an experienced executive.

An entrepreneur with an existing customer base has different opportunities from someone beginning their career.

The goal is not to discover the same new opportunity for everyone.

It is to help each person discover possibilities relevant to their circumstances.

11. What Should Business Schools Teach in the AI Era?

The provocation raises an interesting educational question.

Should business schools focus primarily on preparing students for existing professional roles?

Or should they also prepare students to discover and create roles that do not yet exist?

These objectives are not mutually exclusive.

Students still need strong foundations in finance, economics, operations, marketing, ethics and organizational behavior.

But they may also benefit from deliberate practice in:

Problem discovery.

Creative reframing.

Lateral thinking.

Human–AI collaboration.

Experiment design.

Evidence-based decision-making.

Responsible innovation.

Interdisciplinary learning.

A possible classroom experiment

Imagine an MBA course in which students must identify a consequential problem that is not adequately addressed by existing products or services.

They then use PO to challenge the assumptions surrounding the problem.

AI helps them explore alternatives.

Students interview relevant stakeholders.

They evaluate the ideas and test a small intervention.

The final assessment is not merely a business plan.

It is a documented learning process showing:

What was assumed?

What was challenged?

What possibilities emerged?

What evidence was gathered?

What worked?

What failed?

What should happen next?

Such an exercise could complement traditional business education.

It would help students practice creating possibilities, not only analyzing established ones.

12. The Bigger Possibility: From Career Seekers to Opportunity Discoverers

Sam Altman's reported comments raise a question about ambition in a period of technological change.

But the deeper issue extends beyond MBA graduates.

It applies to engineers, teachers, consultants, managers, researchers, entrepreneurs and experienced professionals.

Many of us approach the future using categories inherited from the past.

We ask which existing role will remain attractive.

Which successful company should we imitate?

Which familiar skill should we acquire?

Which established strategy should we follow?

These can be sensible questions.

But they are not sufficient.

PO introduces a different possibility.

What if the most valuable opportunity is not yet represented by a familiar job title, product category or business model?

Generative AI can help us explore such possibilities.

But we must guide that exploration deliberately.

Otherwise, AI may simply generate more polished versions of conventional advice.

This is the distinctive opportunity of combining PO with AI.

PO challenges the frame.

AI expands the possibilities.

Human judgment separates promising directions from attractive speculation.

Experimentation reveals what deserves further investment.

That is the thinking philosophy behind AIYING's PO–AI Thinking Lab.

13. Your PO Challenge

For the next seven days, imagine that your preferred career or business path is unavailable.

Not permanently.

Just as a thinking exercise.

Ask yourself:

“If I could not follow an established success story, what new problem would I choose to solve?”

Then use the complete ChatGPT prompt in this article.

Explore the possibilities.

Investigate the evidence.

Test one direction.

You may discover that your original path remains the best choice.

Or you may uncover an opportunity you had never considered.

Either outcome can improve your decision.

A question for readers

Are we preparing young people to succeed in the world we know—or to discover possibilities in the world that is emerging?

What would you advise an Indian MBA graduate entering the AI era?

And what unconventional opportunity would you personally explore?

Share your thoughts and discoveries in the comments.

About PO Prompts

PO Prompts is an AIYING series exploring the power of deliberate provocation and generative AI to discover new possibilities.

Each episode combines a relatable story, a hidden assumption, a PO provocation, structured movement, AI-assisted exploration, critical evaluation, a practical experiment and a complete reusable ChatGPT prompt.

The series draws inspiration from Edward de Bono's original work on lateral thinking, provocation and movement.

The six-stage FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH workflow is an AIYING adaptation for practical human–AI collaboration.

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

PO Prompts — One Strange Question. A New Possibility.

Don't just ask AI which successful path to follow. Use PO to discover a path that may not yet exist.

AIYING — Accomplishing with AI.

References and Further Reading

1. The Times of India (October 29, 2026). Sam Altman on MBA students, established career paths and emerging AI opportunities.

https://timesofindia.indiatimes.com/technology/tech-news/sam-altman-is-unhappy-with-most-mba-students-that-he-calls-his-chronic-disappointment-says-they-always-want-to-go-do-the-thing-that-/articleshow/135287091.cms

2. Edward de Bono — Serious Creativity. Background on deliberate creative thinking, provocation and movement.

https://www.debono.com/serious-creativity-article

Editorial note: The Indian MBA campus story is fictional. The PO provocation, career possibilities and six-stage AIYING exercise are creative extensions developed for this article, not statements attributed to Sam Altman. Suggested opportunities require independent validation.

PO Prompts #004 — What If Customers Designed the Problems? | Reversing Innovation with AI

2026-10-29T12:21:28.630+05:30

What If Customers Designed the Problems?

One Strange Question. A New Possibility.

What if the greatest business innovation begins not with a better solution, but with a completely different way of defining the problem?

The Story: The Product Nobody Wanted

Six months.

That was how long an ambitious Indian startup had spent developing its new AI-powered productivity application.

The company had a talented engineering team, enthusiastic founders and a clear product roadmap.

The application could organize tasks, summarize meetings, generate schedules, prioritize activities and recommend ways to improve productivity.

The team was proud of what it had built.

The interface was elegant. The technology worked. Early demonstrations received encouraging responses.

Then came the launch.

People signed up.

Some explored the application.

A few praised its features.

But something unexpected happened.

Very few customers returned.

The founders were puzzled.

The product worked exactly as designed.

So why weren't people using it?

The meeting that changed everything

The team assembled to discuss the problem.

The product manager suggested adding more features.

The marketing manager proposed a promotional campaign.

The designer recommended simplifying the interface.

The founder wanted a better onboarding experience.

Each suggestion sounded reasonable.

And each was based on the same assumption:

We have built the right solution. We just need to make customers appreciate it.

Then Nisha, a young designer who had joined the company recently, asked a question.

“Why are we trying to improve something customers may not actually need?”

The room fell silent.

She continued.

“What if we stopped deciding what problems customers should have?”

Someone laughed.

“Customers don't design products. We do.”

Nisha smiled.

“Exactly. That's what we assume.”

Then she introduced an unusual proposition:

“What if customers designed the problems, and companies competed to solve them?”

At first, the idea seemed absurd.

Customers were supposed to buy products, not define a company's innovation agenda.

But as the team explored the idea, something began to change.

They stopped discussing features.

They started discussing customers' lives.

What were people struggling with before discovering the application?

What were they already doing to manage their work?

Which frustrations were important enough to motivate a change?

What problems had the company never thought to investigate?

For the first time, the team was not asking how to sell its product.

It was asking whether it had understood the problem.

And that was the beginning of a very different conversation.

1. The Hidden Assumption Behind Product Development

Many organizations follow a familiar sequence:

Identify an opportunity → Define a problem → Design a solution → Build a product → Ask customers for feedback.

There is nothing inherently wrong with this process.

Successful organizations already use customer research, co-design, observation and experimentation to understand real needs.

Yet even customer-focused teams can become attached to their initial interpretation of a problem.

Once a solution takes shape, subsequent discussions often revolve around improving it.

Should we add another feature?

Should we change the pricing?

Should we redesign the interface?

Should we market the product differently?

These questions may be useful.

But they can overlook a more fundamental possibility:

What if we have defined the wrong problem?

Consider the difference between these two questions.

Conventional question:

“How can we make our productivity application more attractive?”

Alternative question:

“What difficulties do people actually experience when trying to accomplish meaningful work?”

The first assumes the application is the appropriate starting point.

The second leaves open the possibility that customers need something entirely different.

Perhaps their biggest difficulty is not task management.

Perhaps it is unclear responsibilities, constant interruptions, unrealistic expectations, conflicting priorities or decisions delayed by other people.

A better task-management application may not address any of those problems.

This is where deliberate lateral thinking becomes valuable.

2. Enter PO: A Provocation That Reverses the Relationship

Edward de Bono developed lateral-thinking methods to help people move beyond established patterns of thought.

One of his important contributions was the use of PO, a signal that introduces a deliberate provocation.

A PO statement is not necessarily a fact, a recommendation or a proposal to be implemented literally.

It is a thinking device.

It temporarily suspends ordinary judgment so that an unusual idea can become a stepping stone toward new possibilities.

For this episode, we use reversal.

Our PO statement

PO: Customers design the problems. Companies compete to solve them.

This reverses a familiar relationship.

Instead of companies defining problems and presenting solutions to customers, imagine customers having the primary role in framing the challenges organizations attempt to solve.

The idea may initially seem impractical.

Customers do not always know the technical possibilities.

They may disagree about what matters.

Their stated preferences may differ from their actual behavior.

And organizations still need expertise, judgment and commercial discipline.

But those objections are not reasons to abandon the provocation immediately.

They are reasons to explore it carefully.

The purpose of PO is not to prove that customers should literally control every aspect of product development.

The purpose is to discover what becomes possible when we reverse our usual way of thinking.

3. PO–AI Thinking Lab: From Provocation to Practical Innovation

Beginning with Episode #004, PO Prompts follows a refined six-stage Thinking Lab.

The approach draws on Edward de Bono's provocation and movement concepts, then extends the exploration using generative AI, critical evaluation and practical experimentation.

The six stages are:

FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH

Let's apply each stage to our startup.

Stage 1 — FOCUS: Identify the Real Problem

The startup initially described its problem as:

“Our productivity application is not retaining enough customers.”

That is a business symptom.

It does not yet explain why customers leave.

The team assumes that improving the application will improve retention.

But what if the product itself is not addressing a sufficiently important customer need?

Questions worth asking

What problem did we believe customers had?
What evidence supported that belief?
How did customers actually describe their difficulties?
What were they doing before our product existed?
Which assumptions did we make without testing?
Are we improving a solution before validating the problem?

The team discovers several untested assumptions:

Assumption 1: People want another productivity application.

Assumption 2: More features will make the application more valuable.

Assumption 3: Customers understand and describe their needs in the same language as product developers.

Assumption 4: Low retention primarily reflects problems with the product interface or marketing.

Assumption 5: The company should define the problem before involving customers.

These assumptions may or may not be true.

The point is to make them visible.

The first breakthrough

The team's challenge changes from:

“How do we improve our application?”

to:

“What problem are customers actually trying to solve, and how do we know?”

This shift creates room for the provocation.

Stage 2 — PROVOKE: Reverse the Conventional Relationship

Now introduce the PO statement:

PO: Customers design the problems. Companies compete to solve them.

Imagine a world in which companies cannot begin by proposing products.

Instead, customers publish descriptions of difficulties they experience.

Organizations study those difficulties and compete to demonstrate which solutions produce the best outcomes.

A customer might say:

“I lose two hours every day because I don't know which tasks genuinely matter.”

Another might say:

“My work is interrupted by constant requests from colleagues.”

A third might say:

“I have too many tools, and none of them helps me make difficult decisions.”

These statements describe different problems.

Yet the startup's original application might have treated all three as variations of task management.

The reversal forces the company to reconsider its starting point.

Why reversal matters

Reversal interrupts a familiar direction of thinking.

Instead of asking customers to react to a predefined solution, we imagine customers setting the problem agenda.

This does not mean that every customer statement should be accepted literally.

Customers may describe symptoms rather than underlying causes.

Organizations still need to investigate, interpret and validate what they hear.

But the reversal exposes an important principle:

Problem discovery deserves as much creativity as solution development.

Stage 3 — MOVE: Turn the Provocation into New Directions

This is a crucial part of deliberate lateral thinking.

A provocative idea should not remain merely strange or entertaining.

We must move from it toward potentially useful concepts.

De Bono's movement approaches include extracting principles, examining positive aspects, considering consequences, noticing differences and identifying circumstances where an idea might have value.

Let's use several of these approaches.

Movement A — Extract a principle

From the provocation, we can extract this principle:

The people experiencing a problem should have a meaningful role in defining what needs to change.

That principle does not require customers to become professional designers.

It suggests that organizations should create better ways to discover and interpret lived experiences.

A possible direction emerges:

Build the problem-discovery process before building the product roadmap.

Movement B — Examine positive aspects

What might be valuable about customers defining problems?

Organizations could discover frustrations they had not anticipated.

Teams could avoid building features that address weak or imaginary needs.

Customers could feel that their experiences are taken seriously.

Innovation might shift from adding functionality toward improving real outcomes.

These are hypotheses, not guaranteed benefits.

But they point toward experiments worth conducting.

Movement C — Explore consequences

Imagine the startup genuinely adopting this reversal.

What would happen next?

Its product team might spend less time debating features internally.

Its customer interviews might focus more on experiences and workarounds.

Its roadmap might become organized around verified customer difficulties rather than proposed features.

Its success measures might shift from the number of features delivered toward improvements in customers' actual work.

The team might even discover that the most valuable solution is not another application.

It could be a service, a workflow change, an integration or a simpler way of coordinating people.

Movement D — Find special circumstances

Where might customer-defined problems be particularly valuable?

Consider:

Healthcare services, where patient experiences can reveal practical difficulties that standard processes overlook.
Education, where students may identify barriers that curriculum designers underestimate.
Public services, where citizens encounter frustrations that internal administrative metrics fail to capture.
Small businesses, where owners may struggle with problems poorly represented by existing software.
Enterprise technology, where everyday users experience workflow obstacles that senior decision-makers rarely see.

These are areas for exploration, not proof that the approach will always succeed.

The movement insight

We began with an unusual statement:

“Customers design the problems.”

We have moved toward a more practical idea:

Organizations can innovate by giving customers a stronger role in revealing, framing and validating problems before solutions are developed.

That is a meaningful direction for further investigation.

Stage 4 — GENERATE: Use AI to Expand the Possibilities

Now generative AI becomes a thinking partner.

Instead of asking AI for the best product features, we ask it to expand the directions revealed by PO.

Here are five possibilities.

Possibility 1 — A Customer Problem Library

Imagine an organization maintaining a structured collection of customer-described difficulties.

Instead of recording only feature requests, it captures:

What the customer was trying to accomplish
What went wrong
What workaround they used
What consequences followed
What outcome they hoped to achieve

AI could help organize these descriptions into themes.

The organization could then investigate which problems recur, which are most consequential and which are poorly addressed.

Potential value: A stronger connection between product decisions and actual customer experiences.

Important caution: AI-generated clusters are interpretations. They require human review and validation against the underlying evidence.

Possibility 2 — A Problem-First Innovation Marketplace

Imagine customers and communities publishing challenges rather than shopping for predefined solutions.

Organizations, researchers and independent innovators could propose competing approaches.

A challenge might be:

“How can small retailers reduce the time spent reconciling daily transactions?”

Different providers might propose software, process changes, training or automated integrations.

Solutions would be judged against agreed outcomes.

Potential value: Competition shifts toward solving meaningful problems rather than showcasing impressive features.

Possibility 3 — AI-Assisted Discovery of Hidden Frustrations

Customers may not always express their needs as clear problem statements.

They might say:

“This takes too long.”

“I keep forgetting.”

“I don't trust the result.”

“I have to ask three people before anything happens.”

AI could help researchers organize interviews, compare descriptions and generate hypotheses about underlying difficulties.

For example, repeated complaints about forgetting tasks might point toward unclear priorities, excessive workload or fragmented communication.

Potential value: More systematic exploration of what customers mean, not merely what they say.

Important caution: AI cannot reliably determine an unspoken need from a few comments. Every hypothesis must be tested with real users.

Possibility 4 — Customer-Defined Success Measures

Companies often evaluate products using internal metrics such as feature adoption, session duration or engagement.

What if customers helped define what successful outcomes look like?

A customer might care about:

Completing essential work before leaving the office
Making fewer avoidable mistakes
Reducing unnecessary interruptions
Spending less time coordinating routine activities
Feeling more confident about important decisions

AI could help translate these outcome descriptions into candidate measures.

Potential value: Product success becomes more closely connected to meaningful customer results.

Possibility 5 — Products That Evolve Around Changing Problems

Imagine a product that regularly asks users:

“What has become more difficult for you since we last spoke?”

AI could help organize responses, identify emerging patterns and suggest new questions for researchers.

The organization could investigate whether changing customer circumstances require different services, workflows or features.

Potential value: Continuous problem discovery rather than occasional feature-request collection.

Important caution: Customers must understand what information is collected, how it is used and what choices they have. Privacy and consent are essential.

Stage 5 — JUDGE: Which Possibilities Are Worth Pursuing?

Creative thinking should expand possibilities before narrowing them.

But eventually, judgment is necessary.

Not every unusual idea deserves implementation.

Not every AI-generated suggestion reflects a genuine customer need.

And not every customer request represents a commercially viable opportunity.

Let's evaluate our five possibilities.

Possibility	Potential value	Main challenge	First validation step
Customer Problem Library	Better visibility into recurring needs	Incomplete or biased feedback	Review a small sample of actual customer experiences
Problem-First Marketplace	Diverse competing solutions	Attracting participants and defining success	Test one challenge with a small group
AI-Assisted Frustration Discovery	Faster organization of qualitative research	Misinterpretation and hallucination	Compare AI themes with original interviews
Customer-Defined Success Measures	More meaningful outcomes	Selecting reliable measures	Ask customers to rank desired outcomes
Products That Evolve Around Problems	Earlier discovery of changing needs	Privacy and feedback fatigue	Pilot periodic opt-in feedback

These are qualitative judgments for exploration, not evidence-based rankings.

Questions for responsible evaluation

Before selecting a possibility, ask:

Usefulness: Does this address a meaningful customer problem?

Feasibility: Can we test it with available time and resources?

Evidence: What observations support the underlying assumptions?

Risk: Could the approach create privacy, fairness, accessibility or trust problems?

Measurement: What would demonstrate that the approach works?

Alternatives: Is there a simpler or more effective way to achieve the same outcome?

For our startup, the Customer Problem Library may be a sensible starting point.

It is relatively easy to pilot and does not require building a new platform.

The team can begin with real conversations.

Stage 6 — ACCOMPLISH: A Seven-Day Practical Experiment

Let's turn the idea into an experiment.

Experiment: Stop selling the solution. Discover the problem.

Objective: Determine whether direct customer problem discovery reveals important needs that the startup's current product roadmap overlooks.

Duration: Seven days.

Participants: Five to ten customers or prospective customers, selected to represent different relevant usage experiences.

Day 1 — Define the learning goal

Write down the problem the company believes it solves.

For example:

“Busy professionals struggle to manage their daily tasks.”

List the evidence supporting that statement.

Identify what remains uncertain.

Days 2–3 — Interview customers without pitching the product

Ask open-ended questions:

Tell me about a recent workday that felt unnecessarily difficult.
What were you trying to accomplish?
What got in your way?
What did you do to work around the difficulty?
How often does this happen?
What happens when the problem remains unresolved?
Have you tried anything to improve the situation?
What would a meaningful improvement look like?

Avoid leading questions such as:

“Would you like an AI app that organizes your tasks?”

That question introduces a solution before understanding the problem.

Day 4 — Organize the evidence with AI

With appropriate consent and privacy safeguards, provide anonymized interview notes to ChatGPT.

Ask it to distinguish:

Direct customer observations
Reported frustrations
Existing workarounds
Desired outcomes
Possible underlying causes
Unverified hypotheses

Require it to link each theme back to the relevant evidence.

Do not ask AI to invent customer needs.

Day 5 — Compare discoveries with the existing roadmap

Which problems were already known?

Which were underestimated?

Which are not addressed by the current product?

Which assumptions appear unsupported?

Select one surprising problem for further investigation.

Day 6 — Design a small solution experiment

Do not immediately build a major new feature.

Try a lightweight intervention.

For example, if customers struggle with unclear priorities, test a simple daily priority-setting workflow with a few participants.

It might involve a checklist, a short guided conversation or a manual service.

Day 7 — Evaluate and learn

Compare the experimental approach with customers' existing methods.

Ask:

Did participants experience an improvement?
Was the problem important enough for them to change behavior?
What evidence supports the result?
What remains uncertain?
Should the team continue, revise or abandon the idea?

What counts as success?

The experiment succeeds as a learning exercise if it produces a clearer, evidence-supported understanding of an important customer problem.

It does not need to produce a new product immediately.

Sometimes the most valuable accomplishment is discovering that you should not build what you originally planned.

4. The Complete ChatGPT PO Prompt

The following prompt turns this episode into a reusable AI-assisted thinking exercise.

You can apply it to a product, service, customer experience, educational program, organizational process or business model.

Copy and use this prompt

Act as my PO lateral-thinking partner, drawing on Edward de Bono's provocation and movement methods.
My situation: I am developing or improving a product, service, process or experience.
Use this provocation:
“PO: Customers design the problems. Organizations compete to solve them.”
Guide me through six stages:
1. FOCUS: Identify the conventional problem definition and hidden assumptions about who defines customer needs.
2. PROVOKE: Explore the reversal seriously without immediately dismissing it as impractical.
3. MOVE: Use principle extraction, consequences and positive aspects to discover new directions.
4. GENERATE: Develop at least five genuinely different possibilities for discovering and responding to customer-defined problems.
5. JUDGE: Evaluate each possibility for usefulness, feasibility, risks and evidence needed. Clearly separate assumptions from verified customer insights.
6. ACCOMPLISH: Help me design one small, low-cost experiment with a clear success measure.
Ask me questions at each stage. Do not jump straight to solutions.
Do not invent customer evidence, research findings or validation results.
Begin by asking:
“What product, service or customer experience would you like to rethink?”

How to use it effectively

Begin with a real challenge.

For example:

“Our training program has low completion rates.”

“Our customers rarely use our mobile application.”

“Our students are not engaging with online lessons.”

“Our consulting clients often request major changes after reviewing proposals.”

Then allow AI to guide you through the six stages.

At each stage, contribute your own knowledge, experiences and evidence.

Treat AI-generated ideas as possibilities for investigation, not as established facts.

The objective is not to produce a longer list of solutions.

It is to discover a different and potentially more useful way of understanding the problem.

5. Beyond Startups: Where Else Could This PO Prompt Be Useful?

The same provocation can open possibilities in several domains.

Education

Conventional approach: Teachers design learning activities and students complete them.

PO reversal: Students identify the learning difficulties; educators design ways to address them.

This could inspire student-led problem discovery, personalized support and new approaches to curriculum improvement.

Healthcare

Conventional approach: Healthcare organizations design services around clinical and operational requirements.

PO reversal: Patients describe the difficulties of navigating care; organizations investigate how to improve the experience.

This might reveal opportunities in communication, scheduling, accessibility and care coordination.

Clinical safety and professional expertise remain essential.

Government and Public Services

Conventional approach: Agencies define services and citizens navigate them.

PO reversal: Citizens identify recurring service difficulties; agencies redesign processes around validated needs.

This might reveal problems that administrative performance indicators do not fully capture.

Consulting

Conventional approach: Consultants receive a client's stated problem and propose a solution.

PO reversal: Clients and affected stakeholders collaboratively redefine the problem before recommendations are developed.

This could reduce the risk of producing sophisticated solutions to poorly understood challenges.

AI Product Development

Conventional approach: Developers build AI capabilities and search for applications.

PO reversal: Users identify consequential problems; developers determine whether AI is appropriate.

Sometimes the best solution will involve AI.

Sometimes it will involve a simpler process, clearer responsibilities or better information.

The problem should guide the technology—not the other way around.

6. Why PO and Generative AI Work Well Together

Generative AI has an extraordinary capacity to produce ideas, variations, scenarios and alternative formulations.

But generating more ideas is not necessarily the same as thinking differently.

If we ask AI to improve an existing product, it may produce numerous plausible improvements while accepting the product's underlying assumptions.

PO introduces a deliberate disruption.

It changes the direction of exploration.

AI can then help develop the implications of that disruption.

Human judgment determines which possibilities are worth investigating.

Experimentation provides evidence about what actually works.

The PO–AI partnership

PO provokes.

It challenges assumptions and disrupts established thinking patterns.

AI generates.

It helps explore alternatives, connect concepts and develop hypothetical possibilities.

Humans judge.

They bring context, values, responsibility and critical evaluation.

Together, they accomplish.

They turn promising ideas into experiments, decisions and practical improvements.

This is the philosophy behind the AIYING adaptation of PO thinking.

It is not a replacement for Edward de Bono's original methods.

It is an attempt to apply deliberate creative thinking in an age of generative AI.

7. The Bigger Insight: Who Owns the Problem?

Organizations often compete through the quality of their solutions.

Better technology.

More features.

Lower prices.

Faster delivery.

Improved customer experience.

All of these can matter.

But what if a deeper competitive advantage comes from understanding problems that others have overlooked?

Consider two organizations.

Organization A asks:

“How can we make our solution better?”

Organization B asks:

“What important problem are we failing to understand?”

Organization A may improve an existing offering.

Organization B may discover an entirely new opportunity.

Neither question is universally superior.

But when an organization becomes too attached to its existing solution, the second question may be especially valuable.

The PO reversal helps make that question visible.

Innovation does not always begin with a brilliant answer.

Sometimes it begins by giving someone else the opportunity to redefine the question.

8. Your PO Challenge

Think about something you are currently trying to improve.

It could be a product, service, course, business process, family routine or community initiative.

Ask yourself:

Who currently defines the problem?

Then introduce the provocation:

PO: The people affected by the problem become its designers.

What changes?

What hidden assumptions become visible?

What possibilities emerge?

What could you test this week?

Try the complete ChatGPT prompt above and share the most surprising direction you discover.

A question for readers

If your customers, students, employees or users could redefine the problem you are trying to solve, what might they tell you that you have never considered?

I'd be interested in hearing your examples.

About PO Prompts

PO Prompts is an AIYING series exploring how deliberate provocations can help us discover new possibilities with generative AI.

Each episode combines:

One relatable story
One hidden assumption
One deliberate PO provocation
Structured movement toward new possibilities
AI-assisted exploration
Human judgment and evaluation
One practical experiment
One complete, reusable ChatGPT prompt

The series draws inspiration from Edward de Bono's work on lateral thinking, provocation and movement.

The six-stage PO–AI Thinking Lab is an AIYING adaptation that combines these ideas with generative AI and practical experimentation.

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

PO Prompts — One Strange Question. A New Possibility.

AIYING — Accomplishing with AI.

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

2026-10-29T11:22:24.804+05:30

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

2026-10-29T08:15:01.208+05:30

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 #001 — What If Helping at Home Were Forbidden?

2026-10-29T07:49:09.619+05:30

What If Helping at Home Were Forbidden?

One Strange Question. A New Possibility.

The Story

Every morning, Meera prepares breakfast while her family gets ready for the day.

Everyone is willing to help.

“Tell me what to do,” they say.

But Meera must still remember the groceries, plan meals, check supplies, organize schedules and remind everyone about their responsibilities.

One day, PO introduces a strange proposition:

“PO: What if helping at home were forbidden?”

At first, the idea sounds unreasonable. But what new possibilities might emerge if we explore it instead of immediately rejecting it?

The PO Thinking Lab

1. Recognize the Problem

Household work continues throughout the day. Even when family members participate, one person may remain responsible for coordinating everything.

2. Expose the Hidden Assumption

We often assume that when people help with tasks, responsibility is being shared.

But doing a task and taking responsibility for its completion are different.

3. Introduce the PO Provocation

PO: Helping at home is forbidden.

This is a deliberate reversal, not a literal recommendation.

Rather than asking how to get more help, we temporarily imagine a household where nobody can simply wait to be asked.

4. Move Beyond the Impossible

What happens if helping is no longer an option?

Perhaps each capable family member must notice certain needs independently.

Perhaps responsibility could be assigned by activity rather than by individual chore.

Perhaps household members could agree on outcomes without requiring one person to supervise every step.

The provocation generates several possibilities rather than one prescribed answer.

5. Discover a New Principle

Shared work is not necessarily shared responsibility.

A family can distribute tasks while leaving the mental work of remembering, organizing and coordinating with one person.

The new possibility is shared ownership: appropriate responsibilities are planned, completed and followed through by those who take them on.

6. Accomplish — A Practical Experiment

Choose one manageable household activity.

Agree that a capable family member will take responsibility for noticing what needs doing, planning it and completing it.

At the end of the day, discuss what worked, what was difficult and what could improve.

Consider age, ability, available time and caregiving needs.

Try the PO Prompt With AI

“Act as PO, my lateral-thinking companion. Challenge the assumption that sharing household tasks means sharing responsibility. Begin with the provocation that helping at home is forbidden. Guide me through possible consequences and alternative arrangements. Help me identify several practical experiments suited to my household. Ask questions rather than deciding for me.”

Your Turn

What would change in your home if nobody waited to be told what to do?

Share your discovery.

PO Prompts — One Strange Question. A New Possibility.

AIYING — Accomplishing with AI.

POwer Conversations #002 — The Homemaker Who Was Never Finished | From Helping to Shared Responsibility

2026-10-29T07:35:41.661+05:30

The Homemaker Who Was Never Finished

A homemaker works all day. Yet the work never ends.

She asks PO, her lateral-thinking companion:

Homemaker: “Why must I do everything?”

PO: “What if you were forbidden from doing any household work tomorrow?”

An impossible idea sparks a surprising discovery.

What if family members stopped merely helping and started taking ownership?

The thinking shifts:

❌ “I must manage everything.”

✅ “We can share responsibility.”

Inspired by Edward de Bono's PO method, this conversation shows how an unusual provocation can challenge deeply held assumptions.

Try it with AI: Ask ChatGPT to help rethink household responsibilities through lateral thinking.

💬 Your view: Is household work truly shared if one person still has to plan and remind everyone?

POwer Conversations
Small Problems. Surprising Questions. Powerful Discoveries.

AIYING — Accomplishing with AI.

#POwerConversations #LateralThinking #EdwardDeBono #AIYING #SharedResponsibility #CreativeThinking

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

2026-10-29T05:00:00.000+05:30