At this point, the discussion must establish a precise boundary.

Not everything that artificial intelligence can do is equally valuable in a business context.

Usefulness depends on where it is applied.

This slide defines that boundary.

Artificial intelligence becomes most useful in areas characterised by repetition.

Specifically:

• repeated drafting
• repeated structuring
• repeated transformation of information

These are tasks where similar thinking is performed multiple times.

The content may vary slightly.

But the structure remains similar.

This is where efficiency can be gained.

---

Where Value Is Highest

The highest value emerges when work involves:

• predictable patterns
• recurring formats
• consistent output expectations

Examples include:

• emails that follow similar structures
• reports that repeat sections weekly
• proposals that follow defined formats

In these cases, the effort required is not in creating something entirely new.

It is in recreating something already known.

This is where AI becomes effective.

Because it can apply known structures quickly.

---

Where Value Is Limited

Artificial intelligence is less useful where tasks require:

• entirely new thinking
• undefined structure
• complex judgment without precedent

In such cases:

• guidance is less precise
• outputs are less predictable

This does not mean AI has no role.

But its effectiveness is reduced.

---

The Practical Rule

The boundary can be stated clearly:

AI is most useful where work is repeated,
and least useful where work is undefined.

This rule is operational.

It guides application.

---

Example — Email Vs Strategy

Consider two tasks:

Task A — Writing recurring client emails

• structure is similar each time
• tone is consistent
• purpose is defined

This is highly suitable for AI support.

Task B — Defining a new market entry strategy

• variables are unknown
• structure is not fixed
• judgment is critical

This is less suitable for direct AI execution.

---

What This Changes

Without this boundary:

• AI may be applied randomly
• results may be inconsistent
• expectations may be unrealistic

With this boundary:

• application becomes targeted
• results become predictable
• efficiency improves

---

Role In the Full Model

This slide connects earlier concepts to practice:

• friction identified
• repetition understood

Now:

• application is targeted

This ensures that effort is directed where it produces results.

---

Usefulness Boundary

High Usefulness

Repeated Tasks
→ Structured Work
→ Predictable Output

Low Usefulness

Undefined Tasks
→ No Structure
→ Unpredictable Output

---

Key Principle

Apply AI where work repeats,
not where work is undefined.