Meta Skills #001 — Apply Before You Learn: The New Learning Skill in the AI Age

AIYING | META SKILLS SERIES #001

Apply Before You Learn: The New Learning Skill in the Age of AI

How lateral thinking and generative AI can transform learning from preparation for action into learning through action.

Series: Meta Skills in the Age of AI & Lateral Thinking

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1. The Learning Assumption We Rarely Question

For generations, education and professional development have followed a familiar sequence:

LEARN → PRACTISE → APPLY

First, acquire knowledge. Then develop proficiency through practice. Finally, apply what you have learned to solve a real problem.

This sequence has served us well. In many disciplines, particularly those involving safety, complex foundations, or professional responsibility, it remains essential.

But is it the only possible sequence?

Consider a graduate who wants to create an AI-powered application. The conventional route may begin with programming fundamentals, databases, APIs, machine learning, and software engineering.

Months later, the graduate might attempt to build an application.

Yet the desire to build something meaningful was present from the beginning.

Why must application always wait until learning is complete?

2. Enter Lateral Thinking: Challenge the Sequence

Edward de Bono's lateral thinking encourages us to examine established patterns rather than merely improve our performance within them.

One of its important devices is PO, a deliberate provocation that allows us to explore an idea without immediately judging whether it is correct or practical.

Let us apply this to learning.

PO: APPLY BEFORE YOU LEARN.

What if application were the starting point of learning rather than its final destination?

This is not a recommendation to abandon knowledge, foundations, or preparation.

It is a deliberate reversal intended to produce new possibilities.

Instead of asking:

"What must I learn before I can begin?"

We ask:

"What can I begin doing, and what must I learn to make progress?"

The change appears small. Its implications can be substantial.

3. Movement: From Curriculum-Driven to Accomplishment-Driven Learning

In lateral thinking, provocation is followed by movement. We do not stop at the unusual statement. We use it to discover a workable concept.

The principle emerging from our provocation is simple:

A desired accomplishment can help determine the learning journey.

Consider two approaches.

Conventional Sequence	Application-First Sequence
Choose a subject	Choose an accomplishment
Follow a predefined syllabus	Identify the knowledge needed
Practise selected exercises	Attempt a real task
Apply after preparation	Learn, test, and improve during application
Demonstrate acquired knowledge	Demonstrate both understanding and accomplishment

The second approach does not eliminate structured learning. It changes how learning needs are discovered and prioritized.

4. Generative AI Changes What Is Possible

Application-first learning is not entirely new. Apprenticeships, project-based education, experiential learning, and learning by doing have long demonstrated the value of practical experience.

What is new is the accessibility of generative AI as an interactive learning assistant.

AI can help a learner:

• Clarify the desired accomplishment.
• Identify missing knowledge and prerequisites.
• Break a complex task into manageable steps.
• Explain unfamiliar concepts when they become relevant.
• Generate examples and practice exercises.
• Review work and suggest improvements.
• Explore alternative approaches.
• Reflect on what has actually been learned.

Previously, a learner might have needed a teacher, mentor, textbook, and multiple resources to coordinate this process.

Generative AI can help bring many of these functions into one interactive workflow.

However, AI explanations and generated solutions can be incorrect. Learners must still verify important information, test results, and develop independent understanding.

5. A Practical Example: The Graduate Who Wanted to Build a Chatbot

Imagine a graduate with no programming experience who wants to build a chatbot that answers questions about a college course.

The conventional response might be:

"Learn Python, APIs, databases, and AI engineering first."

Our application-first response is different.

Begin with the smallest useful version of the chatbot.

Step 1: Define the accomplishment

The chatbot should answer five frequently asked questions using a small set of approved course information.

Step 2: Discover the learning requirements

Ask AI to identify the minimum concepts, tools, and skills required to build a simple prototype.

Step 3: Build a small version

Begin with a no-code prototype or a simple program, depending on the learner's starting capability.

Step 4: Learn at the point of need

When the learner encounters an unfamiliar concept, such as an API, the immediate task creates a reason to understand it.

Step 5: Verify understanding

The learner must explain how the chatbot works, what information it uses, where errors can occur, and what limitations remain.

Step 6: Improve the accomplishment

Test the chatbot with real questions, identify failures, and improve its responses.

The outcome is not merely a prototype.

The learner has also developed a process for discovering, acquiring, and applying knowledge.

That process is the meta skill.

6. The Deeper Meta Skill: Learning to Learn

Learning to learn is not simply the ability to remember information quickly.

It includes the ability to:

• Recognize what one does not know.
• Identify what is worth learning.
• Choose an effective learning strategy.
• Seek and evaluate appropriate assistance.
• Monitor progress and understanding.
• Transfer knowledge to unfamiliar situations.
• Revise the learning approach when circumstances change.

In the AI age, this capability becomes especially important because people may encounter unfamiliar tools, tasks, and disciplines more frequently.

Instead of preparing once for a fixed professional role, learners may need to develop new capabilities repeatedly throughout their careers.

We can describe this shift as:

FROM LEARNING A SKILL

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TO LEARNING HOW TO BECOME SKILLED

7. An AI Prompt You Can Use Today

Try this prompt with a small project you have wanted to undertake but have postponed because you believe you lack the necessary skills.

AIYING META SKILLS PROMPT #001

I want to accomplish the following:

[Describe your desired outcome]

My current knowledge and experience are:

[Describe your starting point]

Act as my application-first learning coach.

1. Help me define the smallest meaningful accomplishment.
2. Identify the essential prerequisites and any safety or foundational knowledge I must acquire before starting.
3. Break the accomplishment into five manageable steps.
4. For each step, identify only the knowledge and skills needed at that stage.
5. Explain unfamiliar concepts with simple examples.
6. Give me a practical task before providing a complete solution.
7. Ask me to explain what I have understood.
8. Help me test the result and identify mistakes.
9. At the end, summarize the skills I acquired and the gaps that remain.

Do not simply complete the task for me. Help me become capable of completing and explaining it.

8. A Five-Minute Meta Skill Experiment

You do not need to begin with a complex technical project.

Choose something small:

• Create a personal expense tracker.
• Build a simple web page.
• Analyse a small dataset.
• Prepare a presentation on an unfamiliar topic.
• Automate one repetitive spreadsheet task.

Spend five minutes defining the accomplishment and asking AI for the first practical step.

Then complete that first step, which may take longer.

Record three observations:

1. What did I discover that I needed to learn?
2. What did I accomplish?
3. What can I now do or explain without AI assistance?

The third question is especially important.

AI-assisted completion is not automatically evidence of learning.

The learner must demonstrate some increase in independent capability.

9. Judgment: When Application-First Learning Is Not Enough

A lateral provocation is useful because it opens possibilities, not because it must always be adopted literally.

Application-first learning works particularly well for exploratory projects, prototypes, familiar low-risk tasks, and situations where immediate feedback is available.

But there are important limits.

In medicine, aviation, electrical safety, cybersecurity, and other high-stakes fields, essential foundations and supervised training cannot simply be bypassed.

Even in software engineering, learners who rely entirely on AI-generated code without understanding its behavior may create unreliable or insecure systems.

The practical conclusion is therefore not:

"Never learn before applying."

It is:

"Do not assume that all learning must precede all application."

A more flexible learning model may alternate between preparation, application, reflection, and deeper study.

10. From Learning to Operacy

Edward de Bono introduced the concept of operacy to emphasize the capacity to make things happen through effective thinking and action.

In his work, thinking is not limited to analysis, criticism, or demonstrating that an answer is correct. It also involves designing possibilities, choosing courses of action, and accomplishing useful results.

This provides an important connection to our series.

Generative AI may help people access information and attempt tasks that previously required substantial preparation.

But the human challenge remains:

Can we define worthwhile outcomes, develop the necessary understanding, make sound judgments, and turn possibilities into responsible action?

Application-first learning can become one practical route toward developing that capacity.

11. The Meta Skill Advantage

Individual skills remain valuable. Deep expertise remains important. Foundational knowledge does not become irrelevant because AI can generate an answer.

What changes is the relationship between skills and the ability to acquire them.

Someone who knows one tool can perform tasks using that tool.

Someone who has learned how to learn can approach unfamiliar tools and develop new capabilities.

Someone who combines learning agility, lateral thinking, sound judgment, and AI assistance may be better equipped to move across changing professional situations.

This is the larger opportunity of meta skills.

THE META SKILL TAKEAWAY

Do not wait until you know everything before beginning something meaningful.

Begin with a suitable accomplishment. Discover what you need to know. Learn deliberately. Test your understanding. Improve through action.

The ultimate outcome is not merely completing the task. It is becoming more capable.

12. One Question to Carry Forward

Consider something you have been postponing because you believe you are not yet sufficiently prepared.

What is the smallest safe and meaningful part of it that you could begin today?

That question may reveal a new learning path.

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Coming Next: Meta Skills #002

Thinking About Thinking: Who Is Managing Your Thinking When AI Does the Thinking?

When AI can generate explanations, recommendations, and solutions, an important human capability is knowing when to question, redirect, or change the thinking process itself.

The next episode explores metacognition, deliberate thinking, and the role of human direction in AI-assisted work.

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About the Series

Meta Skills in the Age of AI & Lateral Thinking is an AIYING learning and thought-leadership series exploring the transferable human capabilities needed to learn, think, create, decide, and accomplish in an AI-enabled world.

The series draws inspiration from Edward de Bono's thinking methods and develops contemporary applications involving generative AI.

Original de Bono concepts and new AI-age interpretations are distinguished throughout the series.

AIYING
Think Differently. Accomplish More.

Conceptual References

• Edward de Bono — Lateral Thinking: A Textbook of Creativity.
• Edward de Bono — Serious Creativity.
• Edward de Bono — De Bono's Thinking Course.
• Edward de Bono — Po: Beyond Yes and No.

Editorial note: The application-first learning framework and its generative AI applications are contemporary interpretations developed for this series, not methods attributed directly to Edward de Bono.

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POrable #003 — The Garden That Forgot How to Grow | When Generative AI Stops Growth

AIYING · POrables #003 · Lateral AI

The Garden That Forgot How to Grow

A story about Generative AI, abundance, and the possibility of discovering what nobody has imagined.

From Deadlock to Gateway of Possibilities

Imagine a garden capable of producing any flower instantly.

Every colour. Every shape. Every impossible combination. Nothing ever fails.

Would that be the most successful garden in the world?

Or could something essential disappear when everything becomes possible?

PO: Generativity is sterility.

This is not a statement to accept or reject. It is a deliberate provocation. Let us see where it takes us.

The POrable

In a village called Everbloom lived a gardener named Arin.

His garden was famous throughout the kingdom. People travelled for days to see his roses, lilies and orchids.

But Arin had one problem.

Flowers took time to grow. Some seeds never sprouted. Some plants died. Others bloomed too late for the spring festival.

One year, a brilliant inventor arrived with a remarkable machine.

"Describe any flower," she said, "and my machine will create it instantly."

Arin was astonished.

He asked for a blue rose. The machine produced one.

He asked for a golden lily. It appeared.

He asked for a flower that had never existed.

The machine produced a magnificent blossom with silver petals that changed colour in sunlight.

Arin bought the machine.

Soon his garden became the most beautiful in the world.

There were no weeds, no pests, no waiting and no failed harvests.

Every flower was perfect.

And every morning, the machine generated thousands more.

Years passed.

The village prospered. Gardeners became flower designers. Children learned to describe extraordinary blossoms rather than plant ordinary seeds.

The old greenhouses became exhibition halls.

The seed merchants closed their shops.

Nobody noticed that the village's last packet of seeds had been placed in a museum.

Then, one spring morning, a little girl named Mira visited Arin.

She carried a small, brown seed.

"Can you make this grow?" she asked.

Arin smiled.

"Why grow it? Tell me what flower you want, and I will give you a thousand."

"But I don't know what flower it will become," Mira replied.

"Then the machine can invent one."

Mira shook her head.

"I don't want the flower I imagine. I want to discover the flower I cannot imagine."

Arin had no answer.

For the first time in many years, he looked beyond the perfect blossoms surrounding him.

He noticed that the garden had no bees.

No butterflies.

No fallen seeds.

No new shoots emerging unexpectedly from the soil.

The flowers were beautiful. But nothing in the garden was growing.

That evening, Arin called the inventor.

"Your machine has made our garden sterile," he complained.

The inventor examined the machine carefully.

"It produces everything you ask for," she said. "It is working perfectly."

"Then make it produce something that grows."

"Describe what you want."

Arin paused.

For the first time, he understood the difficulty.

He could describe a flower. He could describe a seed.

But he could not describe what an unknown seed might become under conditions nobody had yet encountered.

Mira, who had been listening, suddenly spoke.

PO: A garden should produce no flowers.

Arin laughed.

"A garden without flowers?"

"Yes," said Mira. "What if the purpose of a garden were not to produce flowers, but to make new flowers possible?"

The inventor became thoughtful.

She turned to her machine and entered a different instruction.

Do not generate a flower. Generate conditions in which an unexpected flower might emerge.

The machine remained silent.

Then it displayed something nobody had requested before.

A map of the garden.

It showed where the soil had become lifeless, where insects could return, where rainwater might collect, and where different plants could grow together.

It proposed dozens of experiments.

Some would fail.

Others might succeed.

None promised a perfect flower.

Arin was disappointed.

"Is that all?"

The inventor smiled.

"For the first time, the machine has given you something you cannot simply display."

"What is that?"

"Something to discover."

The next morning, Arin cleared a small corner of his magnificent garden.

He loosened the soil.

Mira planted her seed.

The machine monitored the conditions and suggested experiments, but it did not produce a flower.

Days passed.

Nothing happened.

Then a tiny green shoot appeared.

It was crooked. Its leaves were uneven. And nobody knew what it would become.

The villagers gathered around it.

They had seen a million perfect flowers.

Yet never before had so many people been excited by something so small.

Months later, the village held its annual flower festival.

The machine-generated blossoms were as magnificent as ever.

But the longest queue formed outside a modest patch of soil.

A wooden sign stood beside it.

THE GARDEN OF THINGS NOT YET KNOWN

And beneath it, Mira had written:

"Here, we do not come to see what has been generated. We come to discover what can grow."

The Lateral Thinking Breakthrough

The obvious interpretation of this story is that technology can make people dependent on machines.

But that is not the most interesting discovery.

The deeper breakthrough is a change in the purpose of the machine.

OLD PURPOSE
Generate flowers
Produce desired outcomes

→

NEW PURPOSE
Generate conditions
Enable unforeseen outcomes

The machine was not discarded.

It became more useful by changing what it generated.

The discovery:
Generativity need not mean producing more finished things. It can mean creating the conditions from which new possibilities emerge.

How the PO Worked

In Edward de Bono's lateral thinking, a provocation is not an assertion that must be proved.

It is a device for moving outside an established pattern of thinking.

1. Recognize: The garden produces extraordinary abundance.
2. Expose: We assume a successful garden is one that produces beautiful flowers.
3. Provoke: PO: Generativity is sterility.
4. Move: Could a flower-producing machine eliminate the conditions for future growth?
5. Discover: Generativity can create conditions for emergence rather than finished products.
6. Accomplish: Redesign the machine to support experiments, discovery and independent growth.

The second provocation, "A garden should produce no flowers," produces the decisive movement.

Instead of improving flower production, Mira changes what the garden is for.

Lateral AI: What Does This Mean for Generative AI?

Generative AI can produce remarkable quantities of content, code, images, designs and solutions.

But quantity of output is not the same as the growth of human capability.

Consider three possibilities.

1. Education

Instead of generating an answer for a student, AI could generate an experiment through which the student discovers the answer.

The outcome is not merely an answer. It is a stronger learner.

2. AI Engineering

Instead of generating more code, AI could expose hidden assumptions, propose alternative architectures and design tests that challenge the current approach.

The outcome is not merely more code. It is better engineering judgment.

3. Innovation

Instead of generating a hundred product ideas, AI could help design customer experiments that reveal needs nobody had anticipated.

The outcome is not merely an idea catalogue. It is a discovery process.

A new design question for AI:

What if we evaluated AI not only by how much it generates, but by how much new human thinking, learning and discovery it makes possible?

Try It: The Garden Experiment

Here is a small Lateral AI exercise you can try today.

1. Choose a task for which you normally ask AI to produce a finished answer.
2. Ask AI to identify the assumptions behind the task.
3. Ask it to generate conditions, questions and experiments rather than a finished solution.
4. Choose one experiment and carry it out yourself.
5. Record one discovery that was not present in your original request.

Reusable Lateral AI Prompt

I want to explore the following challenge: [Describe your challenge] Do not immediately produce a finished solution. Instead: 1. Identify the assumptions that shape how this challenge is currently understood. 2. Introduce this provocation: PO: Generativity is sterility. 3. Use the provocation as a stepping stone. Explore what could be lost when we generate more finished outputs. 4. Propose three different conditions under which genuinely unexpected possibilities might emerge. 5. Design one small, low-risk experiment that I can conduct myself. 6. Explain what evidence would help me judge the experiment. 7. End with one question that opens a new direction of thinking. Distinguish speculative possibilities from established facts. Do not claim that an experiment has succeeded before it is tested.

Your accomplishment: Not another AI-generated answer, but one new discovery made possible by AI-assisted thinking.

A Question for Readers

Imagine you have an AI capable of generating a thousand ideas every minute.

What would happen if you stopped asking it for more ideas?

What would you ask it to generate instead?

Conditions for discovery? Questions nobody is asking? Experiments that challenge assumptions? Or human capability to think independently?

Share one surprising interpretation in the comments.

The aim is not to agree with the PO. The aim is to move somewhere new.

The garden did not become truly generative when it could produce a million flowers.

It became generative when it could once again produce something nobody had imagined.

PO: Generativity is sterility.

Perhaps the next frontier of Generative AI is not generating more things.

Perhaps it is helping more things grow.

AIYING | Lateral AI

POrables #003
From Deadlock to Gateway of Possibilities

Inspired by Edward de Bono's lateral thinking principles, especially provocation, movement and concept restructuring.

An original POrable applying lateral thinking to the age of Generative AI.

Think Beyond the Generated.

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

AIYING | FOUNDATIONAL ARTICLE | AI OPERACY

AI Operacy: Beyond AI Literacy

The New Competence of Accomplishing with Artificial Intelligence

Think Constructively. Act Purposefully. Accomplish with AI.

We are learning how to use artificial intelligence.

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

But an important question remains:

Are we becoming better at accomplishing meaningful things?

This question introduces the idea of AI Operacy.

Contents

1. The Missing Dimension of AI Competence
2. Edward de Bono and the Meaning of Operacy
3. What Is AI Operacy?
4. Beyond AI Literacy
5. The Seven Competencies
6. The AI Operacy Practice Cycle
7. AI Operacy in Practice
8. Lateral Thinking Meets Generative AI
9. How Do We Recognize AI Operacy?
10. Your First AI Operacy Challenge
11. Introducing the AI Operacy Initiative
12. Intellectual Foundations and References

1. The Missing Dimension of AI Competence

Artificial intelligence is making sophisticated capabilities increasingly accessible.

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

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

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

The distinction is between producing something and accomplishing something worthwhile.

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

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

This is the opportunity for AI Operacy.

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2. Edward de Bono and the Meaning of Operacy

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

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

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

He proposes that operacy deserves recognition alongside literacy and numeracy.

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

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

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

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

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

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3. What Is AI Operacy?

Proposed definition

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

This definition contains five essential ideas.

It is learnable

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

It combines human and artificial capabilities

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

It is purposeful

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

It includes action and verification

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

It is concerned with meaningful outcomes

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

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

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4. Beyond AI Literacy

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

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

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

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

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

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5. The Seven Competencies of AI Operacy

The proposed framework contains seven interconnected competencies.

1. Purpose

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

2. Perception

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

3. Possibility

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

4. Design

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

5. Judgment

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

6. Action

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

7. Learning

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

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

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6. The AI Operacy Practice Cycle

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

FOCUS

What do we want to accomplish?

EXPLORE

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

PROVOKE

What alternative possibilities could change our approach?

DESIGN

What workable course of action can we construct?

ACT

What appropriate action can we take?

VERIFY

What evidence shows whether it worked?

LEARN

What should we improve, repeat or reconsider?

Different thinking tools can support different activities.

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

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

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

Consider a graduate preparing for employment.

The initial AI request might be:

Give me ten interview tips.

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

Not necessarily.

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

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

Applying the practice cycle

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

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

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

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

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

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

Learn: Identify which improvements should be practiced again.

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

AI supports the process. The learner performs the work.

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8. Lateral Thinking Meets Generative AI

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

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

But the two are not identical.

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

Consider the provocation:

PO: AI must stop answering questions.

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

Possible movements include:

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

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

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

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

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

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9. How Do We Recognize AI Operacy?

AI Operacy should be demonstrated, not merely declared.

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

We therefore propose assessing six dimensions:

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

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

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

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10. Your First AI Operacy Challenge

From an AI Answer to a Real Accomplishment

Time: Approximately 10 minutes for an initial exercise.

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

Use the following prompt with an AI assistant:

I want to accomplish something useful with AI.

My situation is:
[Describe your situation.]

Help me:

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

Ask questions if essential information is missing.

Do not assume AI is always the best solution.

Keep the action small enough to attempt today.

Then carry out the selected action, where appropriate.

Finally, answer four questions:

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

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

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11. Introducing the AI Operacy Initiative

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

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

The initiative will explore:

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

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

Who is AI Operacy for?

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

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

Our central proposition

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

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

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

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

Join the conversation

What is one meaningful thing you have accomplished with AI?

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

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

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12. Intellectual Foundations and References

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

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

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

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

AI OPERACY

Think Constructively. Act Purposefully. Accomplish with AI.

AIYING — Accomplishing with AI

Foundational Article | AI Operacy Initiative

The Effective AI Engineer: Coming Soon | Learn AI Engineering Through 16 Practical Cases

🚀 Coming Soon: The Effective AI Engineer

16 Cases. 16 Engineering Challenges. A New Way to Think About AI.

Don't just learn AI Engineering. Learn to think like an Effective AI Engineer.

AI Engineering is not simply about knowing models, prompts, agents, frameworks or programming tools.

It is also about asking the right questions, challenging assumptions, exploring alternatives and making decisions that create real value.

Introducing The Effective AI Engineer, a new case-based learning course from AIYING — Accomplishing with AI, hosted on Nisarga Innovations.

📚 Learn Through Cases, Not Just Concepts

The course brings together 16 practical, research-grounded cases, combining Edward de Bono's Lateral Thinking with contemporary AI Engineering practices.

Each case begins with a realistic engineering challenge and unfolds through a story that reveals a surprising way forward.

You will explore:

• The engineering problem and its hidden assumptions.

• An unexpected insight or alternative approach.

• The thinking method behind the discovery.

• Engineering choices, trade-offs and supporting evidence.

• Practical exercises and reusable AI prompts.

🎯 Who Is This Course For?

🎓 Students and aspiring AI Engineers / AI Builders — Prepare for an AI career by developing problem-framing, design and decision-making skills alongside technical knowledge.

🌱 Freshers beginning their AI careers — Gain exposure to engineering situations, practical challenges and lessons that can help you approach your first projects with greater confidence.

💼 Experienced engineers, developers and architects — Revisit familiar practices, discover overlooked alternatives and develop fresh perspectives on AI system design and delivery.

💡 A Preview of the Cases

Case #001: The Engineer Who Changed the Question

Case #002: The Engineer Who Removed the Agent

Case #003: The Engineer Who Tested Before Building

Case #004: The Engineer Who Stopped Improving the Prompt

Case #007: The Engineer Who Deleted Half the Tools

These cases illustrate an important principle:

The most effective AI engineering decision is not always to build more. Sometimes it is to think differently.

🔜 Coming Soon!

Whether you are preparing to enter AI Engineering, taking your first steps, or bringing years of experience to the field, this course invites you to discover a different way of approaching AI engineering challenges.

🔗 Course page: https://learning.nisargainnovations.com/course/effective-ai-engineer

By Ravindra Dastikop

AIYING — Accomplishing with AI

#EffectiveAIEngineer #AIEngineering #AIBuilder #GenerativeAI #LateralThinking #CaseBasedLearning #AICareers #Freshers #AIYING

The AI Harness That Made the Model Less Important | The AI ThinkShift #012 |

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

The AI Builder Who Built Nothing | The AI ThinkShift #010 |

The Deadline That Came First | The AI ThinkShift #009

The Mistake That Taught the Lesson | The AI ThinkShift #008

The Money You Saved by Spending | The AI ThinkShift #007 |

The Boss Who Stopped Giving Instructions | The AI ThinkShift #006

The Exam You Never Studied For | The AI ThinkShift #005 |

The To-Do List That Did Nothing | AIYING Confluence #004 |

The Meeting That Never Happened | AIYING Confluence #003 |

The Job You Never Applied For | AIYING Confluence #002 |

The Job You Never Applied For

AIYING Confluence #002 | Lateral Thinking × Generative AI

#AI #Career #AIYINGConfluence #LateralThinking #GenerativeAI #JobSearch #CareerGrowth #FutureOfWork #CreativeThinking #JobHunting #ThinkDifferently #AIYING

Learn AI Without Studying AI: Lateral Thinking × Generative AI | AIYING Confluence #001 |

The Best AI Engineer Writes Less Code | Lateral AI Thinking #001

The Effective AI Engineer #016: The Engineer Who Kept the Decision Open

The Effective AI Engineer · Case 16 of 16

The Engineer Who Kept the Decision Open

Applying Lateral Thinking to Create Meaningful Impact with AI

By Ravindra Dastikop
Thinking method: Six Thinking Hats
Engineering focus: evaluating AI pilots and deciding their next use

A successful pilot earns a decision about its next use, not automatic permission to expand.

An AI pilot can produce convincing demonstrations and useful results while leaving important questions unanswered. The professional task is to establish which uses the evidence supports, which need further work and which should stop.

Research-grounded fiction: The people, organisation and events are illustrative. Six Thinking Hats comes from Edward de Bono; the evaluation and release practices are contemporary engineering applications. The fictional pilot is not a report about the organisations cited.

Contents

1. The situation and learning outcomes
2. The story
3. The thinking method: Six Thinking Hats
4. Making the thinking visible
5. Comparing the next-step options
6. Research evidence and limits
7. Practice and evaluation
8. Professional benefit and transfer
9. AI Continuation Decision Record
10. Final course application
11. References

1. The situation and learning outcomes

A company had completed a pilot of an AI assistant for its customer support team. The assistant retrieved guidance and prepared response drafts. Specialists reviewed each draft before sending it.

The launch presentation showed encouraging results:

• Drafts appeared quickly.
• Many specialists found them useful.
• Routine questions often received suitable responses.
• Usage had increased during the pilot.

The proposed next step was to expand the assistant across the support department.

Meera, the engineer responsible for evaluation, had helped build the system. She wanted it to succeed. She also knew that the pilot results did not describe one consistent experience.

What you will learn

• Interpret pilot results within their actual scope.
• Distinguish useful evidence from unsupported conclusions.
• Use Six Thinking Hats to structure a review.
• Generate alternatives to unrestricted expansion.
• Identify questions that require further testing.
• Document a decision with ownership and review conditions.

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2. The story

A convincing demonstration

At the expansion review, the product manager opened with a demonstration. A routine request arrived. The assistant retrieved the relevant guidance and produced a clear draft.

“This is what we can give every specialist,” he said.

A senior support specialist raised a concern. “For standard requests, yes. For unusual ones, checking the draft sometimes takes longer than writing the response.”

The product manager pointed to the positive feedback. The specialist described a difficult case. Someone mentioned the approaching renewal date for the service contract.

The conversation became a contest between examples.

Meera stopped sharing the dashboard.

“We are discussing evidence, enthusiasm, concern and investment in the same breath,” she said. “Can we examine them separately before deciding?”

Blue Hat: define the decision

Meera proposed a short Six Thinking Hats sequence. Everyone would use the same thinking mode together. Nobody would become the permanent optimist or critic.

She wrote the decision:

Which request categories, users and operating conditions are supported by the pilot evidence?

The options included expansion, limited continuation, redesign, further investigation and stopping a particular use.

The purpose was to reach a decision the evidence could support. Agreement with the original launch proposal was not an acceptance requirement.

White Hat: establish what is known

The team inspected what its dashboard actually measured.

“Response time” meant time until a draft appeared. It did not include the specialist’s review, correction or follow-up work.

The positive feedback came from employees who answered an optional survey. Several participants had not responded.

The pilot had also included many routine requests and relatively few unusual ones.

The existing records were useful, but they did not establish department-wide improvement.

Meera separated three statements:

1. Routine cases frequently produced usable drafts.
2. Some specialists reported substantial correction effort.
3. The total effect on completed work remained insufficiently measured.

The third statement was uncomfortable. It was also more accurate than declaring either success or failure.

Red Hat: acknowledge reactions

Under the Red Hat, participants briefly expressed their feelings without being required to justify them.

The product manager felt proud and impatient to move forward.

A specialist felt relieved when a useful draft appeared, but anxious when a polished response concealed an uncertain interpretation.

Meera felt protective of the work her team had invested.

These reactions mattered to the discussion. They did not become evidence of performance.

Yellow Hat: identify supported value

The team examined where the assistant could help. For routine requests, retrieved guidance and an editable draft could reduce repetitive preparation.

Newer specialists could see useful examples of how to structure a response. The assistant could also make relevant documentation easier to find.

Each benefit was recorded with a condition. A draft was useful when the guidance applied, the response was accurate and review effort remained reasonable.

The team could recognise those benefits while continuing to investigate unresolved categories.

Black Hat: examine reasons for caution

The team examined weaknesses and failure conditions. Unusual requests sometimes required policy interpretation that the assistant handled inconsistently.

Some drafts combined applicable guidance with a plausible but unsupported instruction.

Reviewers could miss such additions, particularly when the rest of the response looked familiar.

Expansion could also change the workload. More users and a different request mix could create conditions the pilot had not exercised.

The specialist who had raised concerns now helped identify categories where the system looked dependable. The product manager helped specify conditions under which expansion would be inappropriate.

Participants were changing thinking activities rather than defending fixed roles.

Green Hat: generate other next steps

The original choice had been framed as expand or do not expand. The team now generated alternatives:

• Expand only routine categories with adequate evidence.
• Offer source retrieval without drafting for selected complex cases.
• Use a structured preparation form where required information was predictable.
• Keep uncertain categories with the existing specialist workflow.
• Redesign the review interface to make unsupported additions easier to detect.
• Stop drafting for categories where correction burden exceeded its value.

Not every idea was selected. The team had nevertheless developed several ways to preserve useful work.

Return to the White Hat: close a critical evidence gap

Discussion could not resolve the missing measurement of total handling effort.

The team arranged a further comparison using representative tasks. Where practical, it assigned comparable work across assisted and existing workflows. It accounted for differences in task difficulty and reviewer experience.

It measured work through completion, including correction and rework. It also checked quality separately. Faster handling could not compensate for failing an essential requirement.

The fictional comparison supported a narrower conclusion than the original launch presentation.

Some routine categories justified limited expansion. Other categories required redesign or more evidence. A small category was removed from AI drafting because its review burden and errors did not support continued use in that form.

Blue Hat: make a bounded decision

At the follow-up review, Meera presented the decision by category.

Each approved use had:

• A defined scope.
• Evidence supporting continuation.
• A responsible owner.
• Monitoring requirements.
• Conditions for pausing or changing the use.
• A review date.

The team retained useful components from the discontinued category, including its test cases and organised source material. Stopping one implementation did not erase the learning.

The product manager looked at the revised plan. “We are expanding less than we expected.”

“We are expanding what we can currently support,” Meera replied.

“And the rest?”

“We have specified what would change the decision.”

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3. The thinking method: Six Thinking Hats

Edward de Bono’s Six Thinking Hats separates different thinking activities so that they can receive deliberate attention. [1]

The hats represent temporary modes, not personality types. Participants can examine the same subject through different modes without becoming permanently identified with one position.

Six thinking modes applied to the pilot decision

Hat	Thinking activity	Application in the case
Blue	Organise and direct the thinking	Define the decision, sequence the discussion and record the outcome
White	Examine information and information needs	Inspect measures, missing evidence and limitations of the pilot
Red	Express feelings and intuitions	Acknowledge pride, anxiety and attachment without treating them as facts
Yellow	Explore value and reasons for benefit	Identify useful applications and the conditions supporting them
Black	Examine difficulties and reasons for caution	Identify failure conditions, review burden and limits of expansion
Green	Generate alternatives	Develop narrower uses, redesigns and different operating arrangements

The sequence used in this case

Blue → White → Red → Yellow → Black → Green → White → Blue

This is one practical arrangement, not a mandatory order. The team returns to the White Hat when it discovers a question requiring additional evidence.

Method distinction: Six Thinking Hats structures the examination. It does not calculate the correct decision, guarantee consensus or replace experiments, specialist judgment and accountable ownership. No PO statement is required in this case.

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4. Making the thinking visible

1. Define the decision precisely.
   Replace “Was the pilot successful?” with a question about particular uses, users and conditions.
2. Inspect what the measures mean.
   Time to draft and time to completed work answer different questions.
3. Separate observations from conclusions.
   Positive feedback supports a claim about reported experience. It does not independently establish productivity or quality.
4. Acknowledge investment and emotion.
   Pride, fatigue and anxiety can be expressed without being disguised as evidence.
5. Explore benefits and weaknesses deliberately.
   Give each appropriate attention rather than allowing them to cancel each other through argument.
6. Generate additional options.
   Narrowing, redesigning or changing a function may preserve value.
7. Obtain evidence the meeting cannot supply.
   A structured conversation cannot fill missing performance data.
8. Make the decision revisable.
   Record its scope, owner, review conditions and the evidence that could change it.

Reusable prompt

We need to decide the next use of this AI system: [describe it].

Structure the review using Six Thinking Hats. Begin by defining the decision and available options.

Separate recorded facts, missing information, feelings, potential benefits, reasons for caution and alternative designs.

Do not assign permanent hats to participants or invent evidence. Identify which questions require testing outside the meeting.

Finish with a decision record stating supported scope, unresolved limitations, ownership, monitoring and conditions for reconsideration.

Do not force consensus or treat completion of the exercise as proof that deployment is justified.

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5. Comparing the next-step options

Possible decisions after an AI pilot

Option	When it may be appropriate	Evidence or preparation needed
Expand a defined use	Quality and operational requirements are met for the proposed scope	Representative results, capacity checks and monitoring
Continue a limited pilot	Potential value exists, but important uncertainty remains	A specific learning question and bounded test
Redesign the interaction	The current approach creates avoidable effort or errors	A demonstrated problem and comparative testing
Reduce the AI function	A narrower capability provides useful support	Tests of the remaining function and handovers
Pause a use	Requirements are not currently met or necessary evidence is unavailable	Clear user communication and an alternative process
Retire an implementation	Continued operation is not justified	A transition plan preserving necessary work and records

Past expenditure should be recorded accurately. It does not establish the value of future expansion.

A use can also require several changes together. For example, limited expansion may depend on a redesigned review interface and a narrower set of supported categories.

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6. Research evidence and limits

Evaluate outcomes as well as execution

Anthropic’s evaluation guidance distinguishes the recorded execution of a run from its final outcome. It also describes combining evaluations with production monitoring, experiments and user research. [2]

These distinctions support examining completed work rather than relying only on a convincing demonstration or the text of a response. They do not establish the performance of the fictional assistant.

Interpret findings within their studied setting

Research reported by Cui and colleagues used randomised field experiments with software developers. The combined analysis found increased completed tasks, while noting noisy individual experiments and differences associated with experience. [3]

These findings illustrate why study conditions, uncertainty and variation matter. They cannot be transferred directly to a customer support assistant or used to justify an unrelated deployment.

The proposition to test

Separating thinking activities and examining evidence at the level of the proposed use can support a more explicit, reviewable decision.

The case illustrates that proposition. It does not establish that Six Thinking Hats improves deployment outcomes, meeting speed or decision accuracy.

Evidence boundary: The pilot observations and comparison results in the story are fictional. The cited sources provide method and engineering context.

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7. Practice and evaluation

Accessible exercise

Choose one AI tool or workflow you already use.

• State the decision you need to make about its future use.
• Record what you know and what remains unmeasured.
• Express your immediate reaction separately.
• Identify benefits and the conditions required for them.
• Examine difficulties and reasons for caution.
• Generate alternatives to continuing unchanged.
• Write what evidence would change your decision.

Technical exercise

Review one AI pilot.

• Define the unit of analysis: task, user group and operating condition.
• Inspect sampling, missing feedback and comparison quality.
• Measure review, correction and rework.
• Check quality against authoritative requirements.
• Include failures, exceptions and abandoned tasks.
• Assess the workload expected after expansion.
• Use a Six Thinking Hats session to organise the review.
• Assign tests for unresolved questions.
• Record a decision for each proposed use.

Evaluation measures

• Successful completion against requirements.
• Total handling effort.
• Correction and rework.
• Error severity and frequency.
• Results across relevant task categories.
• User experience and accessibility.
• Cost per successfully completed task.
• Operational capacity and maintenance effort.

Interpret the comparison carefully

Before-and-after differences can reflect changes in workload, staffing or experience. Use a comparison design appropriate to the claim being made and report uncertainty.

Set acceptance requirements before using results to justify expansion. Document any later changes and their reasons.

Do not use an average result to conceal a category that fails an essential requirement. At the same time, avoid drawing confident conclusions from very small subgroups.

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8. Professional benefit and transfer

For AI engineers

• Interpret results within their actual scope.
• Distinguish model performance from workflow value.
• Make missing evidence visible.
• Preserve useful components while redesigning weak implementations.
• Define conditions for reconsideration.

For technical leaders

• Avoid turning launch reviews into contests between advocates and critics.
• Allocate attention to evidence, value, concerns and alternatives.
• Accept different decisions for different uses.
• Assign responsibility beyond the demonstration.
• Treat stopping an unsuitable implementation as a legitimate outcome.

Limits of transfer

A structured discussion cannot guarantee candour, eliminate power differences or repair a poorly designed experiment. Participants need access to relevant evidence and a genuine opportunity to question conclusions.

Some requirements are mandatory. A benefit identified under the Yellow Hat does not cancel a failure to meet them.

Reflection question

What evidence would make you narrow or stop an AI system you helped build?

Professional takeaway

Make the next decision specific, evidence-based and open to revision.

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9. Reusable artifact: AI Continuation Decision Record

Complete one record for a defined use. Attach evidence or link to authorised records where appropriate.

1. System and implementation version.
2. User group, task and operating conditions.
3. Decision being considered.
4. Acceptance requirements.
5. Evidence sources and comparison method.
6. Missing information and sampling limitations.
7. Total handling effort and quality results.
8. Benefits and their supporting conditions.
9. Difficulties and failure conditions.
10. Feelings or intuitions recorded separately.
11. Alternative implementations considered.
12. Additional tests and responsible owners.
13. Decision and permitted scope.
14. Transition or handover arrangements.
15. Monitoring and pause conditions.
16. Review date and evidence that would change the decision.

Completion check: Explain what is supported, what remains uncertain, who owns the next step and when the decision will be reconsidered.

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10. Final course application

Select one real AI workflow and prepare a reviewable recommendation for its next use.

1. Define the purpose.
   State what the person or organisation needs to accomplish.
2. Examine an established choice.
   Identify its purpose and whether other implementations could serve it.
3. Generate alternatives.
   Include changes to the workflow, information, interaction and AI role.
4. Test the complete task.
   Evaluate outcomes, correction effort, exceptions and operational limits.
5. Investigate failures.
   Separate observations from explanations and seek distinguishing evidence.
6. Prepare for interruption.
   Establish what continues, waits or stops, and how work recovers.
7. Review the next decision.
   Use Six Thinking Hats to examine evidence, reactions, value, difficulties and alternatives.
8. Document the recommendation.
   State scope, ownership, limitations, review conditions and the evidence that could change it.

Final deliverable: An AI Continuation Decision Record supported by task evidence, alternative designs and an explicit account of uncertainty.

The aim is to make a justified next decision. Expansion, redesign, limited continuation and retirement can each be appropriate outcomes.

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References

1. Edward de Bono. Six Thinking Hats. Little, Brown and Company, 1985. Project-supplied source, including the colour functions and discussions of the individual thinking modes.
2. Anthropic. Demystifying evals for AI agents . 9 January 2026.
3. Zheyuan Cui, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng and Tobias Salz. The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers . Microsoft Research publication page, June 2025.

Research note: Sources were consulted during preparation of this case on 6 November 2026. Online material can change. The sources do not validate the fictional pilot or establish a measured benefit from the thinking exercise.

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Develop your engineering judgment

The Effective AI Engineer is a 16-part series combining Lateral Thinking with AI engineering. Each independent case includes a professional scenario, an explained thinking tool, research-grounded discussion and practical exercises.

This final case brings the work back to professional judgment: establish what the evidence supports and decide what should happen next.

Join the course: For course and enrolment details, workshops or professional collaboration, contact Ravindra Dastikop and mention The Effective AI Engineer.

Email: ravindra.dastikop@gmail.com
Cell: 8453212131

Explore more cases in the series →

By Ravindra Dastikop

Lateral Thinking · Engineering Judgment · Meaningful Impact

AIYING — Accomplishing with AI

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