Edward de Bono's work on lateral thinking provides a useful foundation for understanding this shift.
In Lateral Thinking: A Textbook of Creativity, de Bono examines how established patterns of perception can influence the way we approach situations.
Once we recognise a familiar pattern, we tend to follow it.
That efficiency is useful in everyday life. But established patterns can also limit our ability to notice alternatives.
Lateral thinking deliberately explores other ways of organising information and perceiving a situation.
It does not simply mean choosing an unusual answer.
It involves moving away from an established line of thinking so that other possibilities can emerge.
From problem-solving to problem-framing
The student example illustrates an application of this principle.
The original framing directs attention toward concentration techniques.
The alternative framing directs attention toward the design of learning.
Changing the focus changes the possibilities we can explore.
De Bono also distinguishes the generation of ideas from their subsequent evaluation.
We need both.
First, create room for alternatives. Then use evidence, judgment, and practical testing to decide which alternatives are valuable.
Important distinction: The student illustration is an original contemporary example developed for this AIYING series. It is not presented as an example from de Bono's books.
This article is inspired by the following works of Edward de Bono:
Lateral Thinking: A Textbook of Creativity — especially the discussions of dominant patterns, alternative entry points, restructuring, and challenging assumptions.
Serious Creativity — deliberate creative thinking, focus, provocation, movement, and the development of possibilities.
De Bono's Thinking Course — thinking as a skill, alternatives, perception, and practical thinking tools.
The student, workplace, home, business, and AI illustrations are original applications created for this series. They are not quotations or documented case studies from de Bono's books. Suggested techniques are possibilities to evaluate, not guaranteed outcomes.
In de Bono's approach, movement
means using a provocative idea
to move toward new possibilities
instead of immediately judging
whether the idea is correct.
Arun moved through three questions:
First: What are we assuming?
That existing activities must
be completed more efficiently.
Second: What changes if we
suspend that assumption?
We can question whether the
activities are necessary.
Third: What new possibility appears?
Some work can be eliminated,
combined, simplified or redesigned.
Notice the difference.
The conventional approach tries
to improve the performance
of existing activities.
The lateral approach questions
the structure of those activities.
Both approaches have value.
They solve different kinds
of problems.
This fictional story is a contemporary
application of ideas from Edward
de Bono's work on lateral thinking.
Three elements are especially relevant.
Challenging assumptions
Instead of accepting the existing
definition of the problem,
we examine the assumptions
that shape it.
Provocation
A deliberately unusual proposition
interrupts the established
thinking pattern.
Movement
Rather than rejecting the
provocation as impractical,
we explore what useful
directions it suggests.
Once possibilities have been generated,
ordinary evaluation becomes essential.
A proposed change must still be tested
against real constraints and consequences.
Important distinction:
The thinking techniques are drawn
from de Bono's work. Arun, his
circumstances and the specific
workplace experiment are original
illustrative material created
for this series.
You do not need a major
organizational transformation
to experiment with lateral thinking.
Begin with one recurring activity
in your working day.
Your Five-Step Experiment
Choose:
Identify one activity that
regularly consumes your time.
Clarify:
What outcome is this
activity supposed to produce?
Provoke:
Imagine the activity could
no longer be performed.
What would happen?
Move:
Generate at least two
alternative ways to
achieve the intended outcome.
Test:
With appropriate agreement,
try one safe, reversible change
and observe the results.
Measure what matters
Do not measure only the
minutes saved.
Also examine whether the
required outcome was achieved,
whether anyone else acquired
additional work, and whether
important information or
accountability was lost.
A useful improvement should
reduce unnecessary effort
without quietly transferring
its cost elsewhere.
This series is inspired by
Edward de Bono's work on
deliberate creative thinking.
Lateral Thinking:
A Textbook of Creativity —
challenging assumptions,
generating alternatives and
restructuring familiar patterns.
Serious Creativity —
deliberate provocation,
movement and the development
of new possibilities.
PO: Beyond Yes and No —
the role of PO in moving
beyond immediate acceptance
or rejection of an idea.
Editorial note: This is an original
fictional case illustrating the
application of established
thinking methods. It is not
presented as a documented
workplace study or a quotation
from Edward de Bono.
The methodological foundation comes from
Edward de Bono's
Lateral Thinking: A Textbook of Creativity,
first published in 1970.
Three parts of the book are especially relevant
to this episode.
A. Difference between lateral and vertical thinking
De Bono distinguishes between two complementary
ways of using information.
Vertical thinking is selective and develops
a direction through logical steps.
Lateral thinking is generative.
It seeks to open new directions by changing
established ways of perceiving and organizing information.
The distinction is not that one is good
and the other is bad.
Both are necessary.
B. The generation of alternatives
In the chapter titled
The Generation of Alternatives,
de Bono explains that a particular way of looking
at something is only one of several possible ways.
The lateral search for alternatives is deliberately
different from simply finding the first satisfactory solution.
A thinker may find a promising approach and still
continue generating other possibilities.
The purpose is to loosen established patterns,
not merely to select a winner immediately.
C. Challenging assumptions
In Challenging Assumptions,
de Bono extends this principle to the basic ideas
on which our thinking rests.
We may rearrange familiar ideas without questioning
whether those ideas themselves should be reconsidered.
Ananya questioned the assumption that the assignment
required an essay.
That opened the possibility of entirely different
forms of learning and communication.
Authentic methodological sequence:
Recognize an established direction → explore alternatives
→ challenge the assumed framework → generate a new
direction → evaluate its usefulness.
This sequence is an instructional synthesis of
relevant ideas in the book, not a named five-step
framework attributed to de Bono.
De Bono also emphasizes practice.
Understanding lateral thinking intellectually
is not the same as developing the habit of using it.
Generative AI can produce useful answers quickly.
But a well-formed answer does not establish
that the original question was well framed.
If a person asks AI to improve an essay,
the system may continue improving essays
without questioning whether an essay is necessary.
This is not an unavoidable limitation of AI.
The same system can also help challenge assumptions,
generate alternatives and explore unfamiliar directions
when prompted to do so.
Conventional AI use
Lateral AI skill
Ask for an answer
Examine the question
Improve the first output
Generate different approaches
Accept the given format
Reconsider the format
Optimize for polish
Explore alternative purposes
Select quickly
Explore before judging
Produce a deliverable
Seek a useful outcome
AI-age principle:
Do not confuse the quality of an AI-generated answer
with the quality of the question that produced it.
The following AI Thinking Lab applies this principle
to six everyday and professional situations.
These are original contemporary adaptations of
de Bono's thinking principles.
They are not exercises reproduced from his book.
Choose the application closest to your own situation.
Each includes a thinking trap, a reframed objective,
a reusable AI prompt and a small experiment.
The prompts are designed to support exploration.
They do not guarantee better decisions.
Evaluate AI-generated ideas against real requirements,
evidence and practical constraints.
APPLICATION 01 · STUDENT
The student who asked AI to improve an assignment
Situation:
You need to communicate a complex topic for a class assignment.
Hidden assumption:
The best way to demonstrate understanding is to produce
a polished written document.
Lateral shift:
Move from improving the document to reconsidering
how understanding could be demonstrated.
Try this AI prompt
I am a student preparing an assignment.
My topic is: [INSERT TOPIC].
The assignment requirements are: [INSERT REQUIREMENTS].
I want to apply Edward de Bono's lateral thinking
principle of generating alternatives.
First, identify the assumptions in my current approach.
Then propose at least five fundamentally different
ways to demonstrate understanding of this topic.
Do not give five variations of the same format.
Include one unconventional possibility.
For each alternative, explain what different
learning or communication opportunity it creates.
Do not rank the ideas initially.
After generating the alternatives, evaluate them
against my actual assignment requirements.
Recommend one small experiment I could try
before choosing my final approach.
Do not invent evidence or assume permission
to change the assignment format.
Small experiment:
Choose two different formats and create a short prototype
of each. Ask a classmate which one communicates
the central idea more clearly.
Skill demonstrated:
Distinguishing the learning objective from the assumed
form of the assignment.
APPLICATION 02 · JOB SEEKER
The job seeker who kept improving the same CV
Situation:
You have applied for several jobs without receiving
many interview invitations.
Hidden assumption:
The solution is to keep improving the CV.
Lateral shift:
Move from optimizing a document to exploring
different ways of demonstrating employability
and reaching suitable opportunities.
Try this AI prompt
I am looking for a job.
My background is: [INSERT BACKGROUND].
My target roles are: [INSERT ROLES].
My constraints are: [INSERT CONSTRAINTS].
I have mainly focused on improving my CV
and applying through job portals.
Use lateral thinking to challenge my assumption
that improving the CV is the main solution.
Identify at least three hidden assumptions.
Generate six substantially different approaches
to finding suitable opportunities.
Consider approaches such as demonstrating skills,
building evidence of capability, direct outreach,
professional networks and alternative role definitions,
but do not limit the search to these categories.
Do not invent vacancies, employers or contacts.
For each approach, explain its potential,
limitations and practical requirements.
After exploration, help me select two realistic
approaches for a seven-day experiment.
Keep recommendations consistent with
my qualifications and constraints.
Small experiment:
Compare one conventional application with one
alternative approach, such as sending a relevant
work sample to a suitable employer.
Skill demonstrated:
Reframing job search from document optimization
to credible opportunity creation.
APPLICATION 03 · PROFESSIONAL
The professional who asked AI to work faster
Situation:
You spend several hours each week preparing
recurring reports.
Hidden assumption:
The reports must continue in their present form,
only faster.
Lateral shift:
Move from accelerating the existing process
to questioning the purpose of the report itself.
Try this AI prompt
I am a professional responsible for a recurring task.
The task is: [DESCRIBE TASK].
Its intended purpose is: [DESCRIBE PURPOSE].
Its current workflow is: [DESCRIBE STEPS].
I want to use lateral thinking before
asking AI to automate the existing process.
Identify assumptions about why the task
is performed in its present form.
Generate at least five different ways
to achieve the underlying objective.
Include possibilities that eliminate,
simplify, redesign or replace parts
of the current workflow.
Do not assume automation is always desirable.
Separate speculative possibilities from
realistic near-term options.
Evaluate the options for usefulness,
accuracy, human oversight, confidentiality,
cost and implementation effort.
Recommend a small reversible pilot
that could test one promising option.
Small experiment:
Ask one report recipient which information actually
influences their decisions. Prototype a shorter
decision-focused report using non-sensitive data.
Skill demonstrated:
Reframing productivity from doing the same work
faster to achieving the purpose more effectively.
APPLICATION 04 · TEACHER
The teacher who asked AI for better explanations
Situation:
Students are struggling to understand a difficult concept.
Hidden assumption:
The teacher needs a clearer or longer explanation.
Lateral shift:
Move from improving explanations to exploring
different ways students could construct understanding.
Try this AI prompt
I am teaching the following concept:
[INSERT CONCEPT].
My learners are: [AGE OR LEVEL].
Their current difficulty is: [DESCRIBE DIFFICULTY].
I usually explain the concept and provide examples.
Challenge the assumption that a better explanation
is necessarily the best intervention.
Generate six different learning approaches.
Consider discovery, observation, comparison,
experimentation, questioning, collaboration
and visual representation where appropriate.
Do not simply produce six rewritten explanations.
For each approach, identify what learners
would actually do and what understanding
the activity might reveal.
Indicate possible misconceptions and limitations.
Recommend one low-cost classroom experiment
with a simple way to assess learning.
Do not present unverified educational outcomes
as established facts.
Small experiment:
Teach one concept using a brief explanation,
then use an alternative discovery activity.
Check understanding with the same short assessment.
Skill demonstrated:
Reframing teaching from delivering information
to creating opportunities for learning.
APPLICATION 05 · HOMEMAKER
The homemaker who asked AI for a better schedule
Situation:
Daily household responsibilities feel increasingly difficult
to manage.
Hidden assumption:
Every existing task must remain, but needs
to be scheduled more efficiently.
Lateral shift:
Move from managing the existing workload
to reconsidering which activities are necessary
and how household needs can be met.
Try this AI prompt
I want to improve how household responsibilities
are managed.
My recurring responsibilities include:
[INSERT ACTIVITIES].
My main difficulties are:
[INSERT DIFFICULTIES].
My household constraints are:
[INSERT CONSTRAINTS].
Instead of immediately creating a better timetable,
help me challenge the assumptions behind
the present arrangement.
Generate six fundamentally different approaches
to meeting the household's needs.
Explore simplification, shared responsibility,
batching, reducing unnecessary work,
changing routines and other possibilities.
Do not assume every task must continue unchanged.
Consider fairness, affordability, accessibility,
family preferences and practical feasibility.
After exploring alternatives, recommend
one small change that could be tested
for a week without disrupting essential needs.
Small experiment:
Choose one recurring task and ask whether it could
be reduced, shared, redesigned or eliminated.
Test a consensual change for seven days.
Skill demonstrated:
Reframing time management as a question
of household design, not merely scheduling.
APPLICATION 06 · ENTREPRENEUR
The entrepreneur who asked AI for better marketing
Situation:
A small business is not attracting enough customers.
Hidden assumption:
The existing product and customer proposition are correct;
only the marketing needs improvement.
Lateral shift:
Move from promoting the current offer to reconsidering
the customer problem and the value being offered.
Try this AI prompt
I run or plan to start a business.
My current offer is: [DESCRIBE OFFER].
My intended customers are: [DESCRIBE CUSTOMERS].
My challenge is: [DESCRIBE CHALLENGE].
I have been considering better marketing
as the main solution.
Use Edward de Bono's principle of generating
alternatives to challenge this assumption.
Identify hidden assumptions about the customer,
problem, offer, delivery method and revenue model.
Generate at least six substantially different
ways to create value for the intended customers.
Include at least one provocative possibility
that challenges the current business model.
Do not invent customer demand or market evidence.
Separate ideas from assumptions requiring validation.
Evaluate the alternatives for customer usefulness,
feasibility, cost and major risks.
Design one inexpensive customer discovery
or prototype experiment to test a promising idea.
Small experiment:
Interview three potential customers about the problem
they actually experience before investing in another
marketing campaign.
Skill demonstrated:
Reframing business growth from selling harder
to discovering a more valuable proposition.
The following prompt can be used in almost any
personal, educational or professional situation.
Its purpose is not to produce a perfect answer.
It is to create a deliberate opportunity
to explore different directions.
I want to practise problem reframing using
Edward de Bono's lateral thinking principles.
My current situation is:
[DESCRIBE SITUATION].
The solution I am currently considering is:
[DESCRIBE SOLUTION].
My real objective is:
[DESCRIBE OBJECTIVE, IF KNOWN].
Please guide me through these stages:
1. OBJECTIVE
Distinguish what I want to accomplish
from the solution I have assumed.
2. ASSUMPTIONS
Identify at least three assumptions
embedded in my current approach.
Ask me to verify important assumptions.
3. ALTERNATIVES
Generate at least five fundamentally
different ways to approach the objective.
Do not merely vary the wording or format
of the same solution.
4. EXPLORATION
Include one unconventional or provocative
possibility. Explain what new direction
it might open without treating it as
an immediately practical recommendation.
5. DELAYED JUDGEMENT
Do not rank or reject the ideas
until we have explored their possibilities.
6. EVALUATION
Now compare the alternatives against
the actual objective, evidence,
constraints, risks and feasibility.
7. ACCOMPLISHMENT
Recommend one small, reversible,
real-world experiment to test
a promising approach.
Distinguish facts, assumptions and
speculative suggestions.
Help me think differently.
Do not make the final decision for me.
Important:
The prompt is a contemporary AIYING adaptation.
It is not an original prompt or formal framework
published by Edward de Bono.
Lateral thinking is not an argument
for rejecting every conventional solution.
Sometimes the first solution is the best one.
Sometimes an essay is exactly what is required.
Sometimes improving a CV, automating a report
or developing better marketing is entirely appropriate.
The important difference is whether the choice
has been made deliberately.
A lateral thinker explores alternative directions.
A responsible thinker then evaluates them
against the real objective, evidence,
constraints and consequences.
In the AI age, both abilities matter.
The danger is not that AI gives us the wrong answer.
It is that we may become exceptionally good at
answering a question we never needed to ask.
Before asking AI to improve something,
ask whether that something is what needs improving.
And before asking for a better answer,
consider whether there is a better question.
One action you can take today
Open an AI conversation in which you have repeatedly
asked for improvements.
Stop improving the current output.
Ask:
"What assumption am I making about the kind
of solution I need?"
Then deliberately explore five alternative directions.
You may return to your original approach.
But this time, it will be a choice rather
than an unquestioned default.
Primary source:
Edward de Bono,
Lateral Thinking: A Textbook of Creativity.
First published by Ward Lock Education, 1970;
Penguin Books reprint, 1990.
Relevant sections of the uploaded edition:
Introduction
Difference between lateral and vertical thinking
The Generation of Alternatives
Challenging Assumptions
The original book supports the distinction
between generative and selective thinking,
deliberate alternative generation,
challenging established patterns,
and practising lateral thinking as a skill.
Contemporary interpretation:
The applications to generative AI, prompting,
job search, teaching, productivity,
household management and entrepreneurship
are original AIYING adaptations.
Evidence boundaries:
The Ananya story is fictional.
The prompts and exercises are proposed
learning activities, not empirically
validated interventions.
No claim is made that de Bono anticipated
modern generative AI or endorsed
the applications presented here.
Every time Captain Rao reached port, his ship stopped earning.
For days, cranes lifted cargo out and lowered new cargo in.
The engines rested. The crew waited. The next voyage slipped
further down the calendar.
At a meeting, everyone had a suggestion.
“Faster cranes.”
“More loading crews.”
“Better containers.”
Then a young designer asked, “What if we loaded the ship at
the dock while it was still at sea?”
Someone laughed.
“Which is it? At the dock or at sea?”
“I don’t know yet,” she replied. “But which part actually
needs to stay?”
She drew a ship on the whiteboard. Then she drew a line
between its cargo section and the section containing the
engines, navigation equipment and crew.
“What if these could separate?”
The room grew quiet.
She sketched several interchangeable cargo units.
One could remain at the dock for unloading and reloading.
Another, already prepared, could connect to the powered
section for the next voyage.
While one cargo unit was being handled in port, the engines
and navigating crew could be at sea with another.
The captain leaned forward.
“All this time, we’ve been asking how to finish loading sooner.”
He pointed to the line through the drawing.
“You’re asking how much of the ship needs to wait.”
They left the meeting with a different design problem—and
a concept to investigate.
A fictional retelling of the supplied lateral-thinking
example. The ship design is a possibility to explore.
For three months, Meera applied for jobs. Every reply said some
version of the same thing:
“You need experience.”
She stared at the latest rejection.
“How do I get experience if nobody lets me work?”
Then a ridiculous thought crossed her mind.
“What if the company that rejected me became my first client?”
She reopened the email. It came from a small furniture business
looking for someone to handle customer communications.
Instead of sending another request for a chance, she visited
its website.
Delivery times were unclear. Assembly instructions were difficult
to find. The returns page contradicted the FAQ.
She drafted a clearer version of one page using only information
already available on the site. Wherever the details were uncertain,
she added a question rather than inventing an answer.
Then she wrote to the owner.
“Thank you for considering my application. While exploring your
website, I noticed a few places where customers might get confused.
I’ve attached one sample rewrite. Would a small, paid project to
improve these pages be useful?”
The owner replied the next morning.
“We’ve been answering these questions on the phone every day.
What would you charge?”
They agreed on a fee, a deadline and five pages.
Two weeks later, Meera had her first payment, a completed project
and permission to use it in her portfolio.
When the next application asked for experience, she finally had
something to put in the box.
An original fictional Porable inspired by Edward de Bono’s PO method.
A wealthy businessman wanted his daughter to marry someone from a rich
family. When he discovered she was dating a student barely managing his
rent, he told her to end the relationship.
She wondered, “What if Dad actually gave him the money to marry me?”
Ridiculous. But the thought gave her an idea.
“Dad, you’re right,” she said. “I should marry the richest guy. But
expensive cars and Instagram photos don’t prove who really has money.”
Her father agreed.
“Give each of them a substantial cash gift,” she suggested. “Then watch
what changes. If someone upgrades his whole lifestyle, the money clearly
made a big difference. If nothing changes, he’s probably already wealthy.”
Her father liked the idea.
“Of course,” she added, “we should include my student boyfriend as a
comparison. We already know he’s broke. His reaction will help us check
whether the test works.”
The transfers went through.
While her father watched the other men for signs of new spending, his
daughter and the now financially secure student quietly got married.
A modern retelling of the story supplied from Edward de Bono’s
lateral-thinking work. The PO above preserves the wording of the
supplied passage.
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.
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.
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.
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.
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.
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.
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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