Hey friends. Happy Wednesday.

A founder said something to me recently that felt simple, but revealed something structural.

“I’ve gotten really good at prompting.
But I’m not sure I’m building better products.”

That sentence captures exactly where many professionals are right now.

We’ve learned how to work with AI.
We know how to structure prompts.
We know how to iterate outputs quickly.

But generating faster is not the same as deciding better.

Prompting got you started.
Product thinking will take you further.

The future is not about writing better prompts.

It is about designing better outcomes.

Because prompting improves output quality.
Product thinking improves decision quality.

And in a world where execution is increasingly automated, decision quality becomes leverage.

Today, we’ll unpack this shift at depth.

Here’s what we’ll explore:

Let’s go deeper.

— Naseema Perveen

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Execution is accelerating. Judgment is not.

AI has collapsed cycles of execution and iteration.

Teams use generative models for writing, design, analytics, prototyping, and workflow synthesis.

Execution happens faster than ever.

Iteration loops have compressed.

Prototyping no longer requires weeks.

Experimentation no longer requires months.

Outputs are abundant.

But outputs are not decisions.

When outputs are pervasive but decision clarity isn’t, organizations run faster in the wrong direction.

This is where the constraint has moved:

Execution is no longer the bottleneck.
Judgment is.

And that’s what product thinking addresses.

AI adoption is growing rapidly, but decision quality remains the defining differentiator

Here are verified signals that align with the core idea of this edition:

1. AI adoption is broad but shallow
McKinsey’s State of AI 2025 Global Survey shows that 70% of companies have adopted AI in at least one business function, but fewer than 30% report that AI has fundamentally changed how strategic decisions are made.
Source: McKinsey & Company 2025 AI Survey

2. Execution efficiency is rising faster than strategic adoption
Organizations deploying generative AI see execution time reduced by up to 40% on tasks like drafting documents, generating code, or synthesizing insights — but this has not consistently translated into improved long-term outcomes without structural decision processes.
Source: McKinsey Operational Insights Report

3. Decision framing and alignment are cited as top challenges
LinkedIn’s Workplace Learning Report 2025 highlights that companies investing in AI skills focused on decision frameworks and workflow redesign outperform those focused only on tool adoption.
Source: LinkedIn Workplace Learning Report 2025

4. Human-AI collaboration is evolving
Research on human-AI symbiosis shows that teams reporting structured decision processes see stronger performance outcomes than those using generative tools primarily for task execution.
Source: Human-AI Collaboration Trends Analysis

Translation:

AI tools are now widely used.
Execution barriers have dropped.
But strategic clarity — the ability to define, measure, and iterate decisions — remains scarce.

That’s where product thinking becomes a competitive advantage.

Two layers, not two skills

Prompting improves output.
Product thinking improves intent.

Prompting asks:

Product thinking asks:

Prompting improves the artifact.
Product thinking embeds that artifact in a system that moves a metric.

Understanding this distinction is the point where AI expertise becomes strategic leverage.

Prompting feels powerful early because improvements are visible.

But structurally, it caps.

Prompting feels powerful early because progress is immediate and visible.

You refine wording.
You improve structure.
You get cleaner outputs.
You see the difference instantly.

That feedback loop creates momentum. It feels like skill acquisition. And it is.

But structurally, prompting operates within a bounded layer. It improves how you communicate with the model. It does not fundamentally change how you define problems.

And that’s where the ceiling appears.

Over time, the marginal returns shrink. The improvements become incremental rather than strategic. The outputs look sharper, but the underlying decisions remain unchanged.

Prompting optimizes expression.

It does not redesign intent.

Let’s break down why that matters.

Early in the AI adoption curve, strong prompting skill created visible advantage. Those who understood formatting, context windows, structure, and iteration loops could extract better results than average users.

But as models improve, the performance gap narrows.

Modern AI systems are increasingly robust to vague or imperfect prompts. They infer context more effectively. They compensate for unclear instructions. They produce competent outputs even when the framing is loose.

This reduces differentiation at the prompt level.

When the baseline rises, tactical refinements matter less.

The competitive edge shifts upstream.

It moves from “How well can you instruct the model?”
to “How well can you define the problem space?”

Prompting skill becomes necessary, but not sufficient.

The differentiator is no longer syntax.
It is strategic clarity.

And strategic clarity does not improve automatically as models improve.

It is entirely possible to generate polished, thoughtful, well-structured outputs that are perfectly wrong.

You can create:

Execution quality and decision quality operate on different axes.

Prompting improves the former.

Product thinking governs the latter.

If the outcome is misdefined, improving the artifact only accelerates misalignment.

In fact, AI can amplify this problem.

Because iteration is cheap, teams may refine the wrong direction faster than ever. They polish instead of question. They optimize instead of interrogate.

The result is activity without impact.

The core issue is this:

Prompting improves how well you answer a question.

Product thinking determines whether the question was worth answering.

That difference is structural.

Every meaningful product decision involves tension.

Growth versus retention.
Speed versus stability.
Automation versus transparency.
Simplicity versus flexibility.

Trade-offs are not optional. They are inherent.

Prompting operates within a request. It attempts to optimize the given objective. It rarely asks what is being sacrificed to achieve that objective.

For example:

“Optimize this onboarding for maximum activation.”

The model can generate stronger calls to action. It can simplify copy. It can reduce friction.

But should activation be maximized at all costs?

What happens to trust?
What happens to long-term retention?
What happens to informed consent in regulated industries?

Those questions require interrogation of the premise, not optimization of the output.

Product thinking makes trade-offs explicit.

It forces the team to articulate:

If we push here, what weakens there?

That articulation is where strategic influence begins.

Because once trade-offs are visible, decisions become deliberate rather than reactive.

Prompting refines execution.

Interrogation reshapes direction.

And direction determines whether effort compounds or decays.

Prompting plateaus not because it stops working.

It plateaus because it operates within defined boundaries.

It improves performance inside the frame.

Product thinking redesigns the frame itself.

In an environment where AI makes execution abundant, the scarce skill is no longer generation.

It is judgment under constraints.

And judgment requires stepping above the prompt layer.

That is where leverage shifts.

Four lenses that upgrade reasoning

Strong product thinkers do not begin with outputs.

They begin with frames.

Here are four lenses they apply before generating anything:

Meaning is shaped by environment

Context answers:

Without context, AI produces generic answers.

With context, mentions become meaning.

For example:

Prompting mode:

Product thinking mode:

This shift changes the entire solution space.

Context reduces noise.
And clarity scales.

Limits define design

Constraints should not be treated as limitations.

They are design inputs.

Consider:

Strong product thinkers incorporate these upfront.

Without constraints, AI optimizes for theoretical clarity.

With constraints, the solution becomes viable.

Every improvement costs something

Trade-offs surface strategic tension.

Increasing onboarding speed may reduce clarity.
Reducing steps may reduce trust.

Product thinkers ask:

Prompting rarely asks this.
Product thinking requires it.

Trade-offs create deliberate strategy.

Behavior change, not deliverables

Outputs are deliverables.
Outcomes are behavioral change.

A feature shipped is not an outcome.

Increased retention is.

Product thinking starts with measurable impact.

AI then becomes a tool for moving metrics.

Without outcome clarity, execution floats.

With outcome clarity, execution aligns.

That is the structural advantage.

From prompt to product architecture

Let’s move from idea to practice.

Original prompt:

Now we elevate it systematically.

Follow-up:
Why 15 percent?
What does that unlock downstream?

Already, reasoning deepens.

Not just “users.”

Break down segments such as:

Each segment shifts emphasis and tone.

Examples:

Constraints shape the space.

They are not cosmetic.

They are structural.

If we emphasize simplicity, we may reduce depth.

If we emphasize rigor, we may increase hesitation.

Product thinking makes that trade-off explicit.

Primary: activation rate
Secondary: time to first investment
Lagging: 30-day retention

Now onboarding is part of a measurement system.

Not an isolated artifact.

Rewrite the instruction:

This is not prompting.

This is architecture.

You are designing a conversion system, not just writing text.

That is the shift.

In your experience, what is the biggest misconception teams have when they start with AI, and how does product thinking resolve it?

We’d love to hear your perspective.

Email your thoughts to: [email protected]
Selected responses will be featured in next week’s edition.

Diagnose where you are

Before you change habits, understand your current mode.

Ask yourself:

If your workflows begin with “Generate X,” you are in output mode.

If they begin with “We need to move Y,” you are in product mode.

Neither is wrong.

Only one scales influence.

Use This to Level Up Your Reasoning System

This worksheet is not about writing better prompts.

It is about upgrading how you think before you prompt.

Most AI use fails quietly because the reasoning layer is weak. The request is unclear. The objective is vague. The constraints are implicit. The trade-offs are ignored.

This structure forces clarity before execution.

Copy this into your workspace. Use it before every major AI-driven task, especially those tied to strategy, product, or growth.

Over time, this becomes a thinking habit.

And habits compound.

Define the change before defining the artifact.

Before you generate anything, articulate the behavioral shift you want.

Not the deliverable.

The change.

Ask yourself:

Be specific.

“Increase engagement” is vague.

“Increase weekly active users by 10% within 60 days” is directional.

This step forces you to anchor work to impact. Without it, you risk optimizing for aesthetics, not results.

Most AI tasks fail because the outcome is implied rather than explicit.

Make it explicit.

Write it down.

If you cannot measure it, you cannot evaluate it.

And if you cannot evaluate it, you cannot improve it.

Meaning is shaped by environment.

Outputs do not exist in a vacuum. They land in the mind of a specific person, at a specific moment, under specific conditions.

Define that environment.

Be concrete.

Are they skeptical?
Time-constrained?
Risk-averse?
Highly technical?
New to the domain?

Context changes tone.
Context changes framing.
Context changes complexity tolerance.

When context is vague, outputs become generic.

When context is precise, outputs become aligned.

Alignment is what makes execution effective.

Boundaries create discipline.

Constraints are not limitations. They are design parameters.

List every boundary that applies.

Legal:
Are there regulatory or compliance requirements that must be honored?

Technical:
Are there architectural limitations or system dependencies?

Brand:
Does tone, positioning, or identity impose boundaries?

Budget:
Are there cost ceilings that restrict implementation?

Timeline:
Is speed a priority? Is there a hard deadline?

When constraints are implicit, solutions drift.

When constraints are explicit, solutions sharpen.

AI optimizes for what you tell it.

If you do not define limits, it optimizes for theoretical perfection.

Your job is to define reality.

Every improvement costs something.

This is the section most teams skip.

And it is where strategic maturity lives.

Ask:

For example:

If we simplify onboarding, clarity improves. Depth may decrease.

If we push aggressive conversion tactics, activation improves. Trust may erode.

Write a one-sentence trade-off statement:

“We are prioritizing X at the cost of Y.”

This sentence forces accountability.

Trade-offs are not mistakes. They are choices.

When you articulate them, you turn implicit risk into deliberate strategy.

That is what separates product thinking from surface optimization.

Design for evaluation, not hope.

Metrics make strategy testable.

Define:

Primary metric:
The core behavior you are trying to change.

Secondary metric:
A supporting signal that confirms direction.

Lagging indicator:
The long-term outcome that validates success.

Leading indicator:
The early signal that predicts whether you are on track.

Example:

Primary: Activation rate
Secondary: Time to first action
Leading: Onboarding completion rate
Lagging: 30-day retention

This layering matters.

Without leading indicators, you wait too long to learn.

Without lagging indicators, you may optimize shallow gains.

AI can generate outputs instantly.

But improvement requires measurement.

Measurement requires structure.

Translate reasoning into instruction.

Now, and only now, rewrite the prompt.

Before:

“Generate X.”

After:

“Design X that moves Y by Z under these constraints…”

This rewrite embeds your reasoning into the request.

It transforms AI from a content engine into a system collaborator.

The model now operates inside your architecture.

That is the difference between using AI and designing with AI.

The power of this worksheet is not in completing it once.

It is in repetition.

Every time you move through these steps, you reinforce structured reasoning:

Outcome → Context → Constraints → Trade-offs → Metrics → Execution.

Over time, this sequence becomes automatic.

You stop reacting with “Generate this.”

You begin thinking with “What are we trying to change?”

That shift rewires how you approach problems.

AI then amplifies your structure instead of compensating for its absence.

Prompting improves answers.

This worksheet improves thinking.

And thinking compounds.

Why this matters long term

Prompting improves productivity.

Product thinking improves authority.

Over time, authority compounds faster than productivity.

In organizations:

Execution roles become less scarce.
Framing roles become more valuable.

AI amplifies both clarity and confusion.

If your reasoning is shallow, AI scales shallow execution.

If your reasoning is structured, AI scales structured decisions.

That determines long-term trajectory.

A deliberate sequence

Month 1: Frame every AI task with a written outcome statement.

Month 2: Define at least one constraint before generating.

Month 3: Document one explicit trade-off per major decision.

Month 4: Link outputs to metrics and evaluate impact.

This sequence rewires thinking.

And thinking rewires influence.

The shift from prompting to product thinking is not just technical.

It is positional.

In a world where:

Writing is automated.
Coding is assisted.
Design is augmented.

Execution becomes less scarce.

When execution becomes abundant, value migrates.

It moves away from those who produce the most outputs and toward those who define which outputs matter.

Operators focus on tasks.

Architects focus on systems.

Operators ask, “How do I complete this efficiently?”

Architects ask, “How does this fit into the larger structure, and what does it unlock?”

As AI compresses production cycles, organizations need fewer pure executors and more decision designers.

The scarce skill becomes reasoning under constraints.

It becomes the ability to:

Prompting keeps you competitive because it improves speed and fluency.

Product thinking makes you indispensable because it improves direction.

And direction determines leverage.

In most organizations, advancement is not tied to how much you produce.

It is tied to how clearly you frame decisions that affect others.

AI accelerates the operator.

It elevates the architect.

The question is which role you are building toward.

Prompting is a skill.

Product thinking is leverage.

Skills improve performance.

Leverage reshapes influence.

Anyone can learn to interact with AI.

Fewer people learn to design systems that integrate AI into decision loops, measurement frameworks, and strategic trade-offs.

In a world of abundant outputs, clarity becomes power.

Clarity is not louder.

It is structured.

Prompting helps you generate.

Product thinking helps you compound.

Prompting got you started.

Product thinking will take you further.

— Naseema
Writer & Editor, The AI Journal

That’s all for now. And, thanks for staying with us. If you have specific feedback, please let us know by leaving a comment or emailing us. We are here to serve you!

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