You’re Using AI to Go Faster.
Your Competitors Are Using It to Decide Better
Every major decision is made under uncertainty.
A strategy is approved. Capital is committed. Teams are hired. Execution begins. The organisation aligns around a direction and moves forward with confidence.
At the time, the decision is sound. It is based on the information available, the alternatives considered, and the judgment applied.
But every decision is also shaped by a constraint that is easy to overlook — exploration bandwidth.
Leaders can only evaluate a limited number of assumptions, scenarios, and alternatives before committing. Time is finite. Attention is finite. So decisions are made within a narrower field of view than what is theoretically possible.
This is not a failure of judgment. It is a structural constraint of decision-making.
Which raises the real opportunity: What if leaders could explore more possibilities, examine more assumptions, and stress-test decisions more rigorously before committing?
In this edition I discuss how AI creates its greatest leverage - not by accelerating execution, by expanding exploration while decisions are still reversible.
1. Most Organisations Use AI After the Point of Leverage
Today, most organisations use AI downstream.
The common pattern:
Authoring reports, plans, and communications faster
Automating repetitive workflows
Accelerating production and delivery
These uses improve productivity, but they also operate within a direction that has already been chosen.
They result in valuable gains, but they optimise execution within an existing path. What they don’t do is improve the quality of the path itself.
Execution speed cannot compensate for decision error. It only makes the consequences arrive sooner.
The real leverage exists earlier - before committing to a path.
2. The Real Constraint: Limited Exploration
Every decision carries uncertainty. Which assumptions are fragile. Which risks are hidden. Which alternatives might be superior.
Human exploration bandwidth is constrained on many fronts. Time pressure, data availability, cognitive load all impact the final decision. This results in leaders evaluating a small number of alternatives than what is possible.
AI removes that constraint. It allows leaders to:
Challenge assumptions that might otherwise go unquestioned
Generate alternative strategic paths
Surface second-order consequences early
Stress-test logic before resources are committed
Exploration bandwidth determines decision quality and AI helps expands it.
3. AI as a System for Exploring Decisions
AI's highest value is not in doing downstream work.
It is in helping leaders see more clearly before committing.
Consider a company evaluating entry into a new market. That decision typically rests on three to five core assumptions. AI can tell you which one is most likely wrong before you've spent anything.
The traditional approach builds and validates a single strategy. The AI-enabled approach interrogates the decision space itself.
AI helps surface
What must hold true for success
Under what conditions the strategy would fail
What alternative paths exist with lower irreversible risk
What blind spots may already be embedded in the plan.
This changes AI's role fundamentally.
From execution engine to exploration engine. From productivity tool to decision quality amplifier.
It’s important to understand here that AI is not replacing judgement, that still belongs to you. What AI is doing is expanding what your judgement can evaluate.
4. The Practical Shift: Using AI Before You Commit
Before committing to a strategic direction
Use AI to surface alternative paths and challenge implicit assumptions.
Example prompt:
"What assumptions does this strategy rely on that could realistically fail in the next 12 months?"
Before allocating significant capital or hiring
Identify risks that are not immediately visible.
Example prompt:
"What would cause this investment to underperform despite strong execution?"
Before launching new initiatives
Surface second-order consequences.
Example prompt:
"If this succeeds faster than expected, what operational constraints will become bottlenecks?"
Before finalising irreversible decisions
Stress-test the logic itself.
Example prompt:
"What would a competitor hope we overlook in this decision?"
This changes the role of AI.
From helping you execute decisions to helping you avoid bad ones.
The Takeaway
Execution multiplies effort. Exploration multiplies judgment.
AI's greatest value is not accelerating execution. It is reducing the probability of committing to the wrong path.
The organisations that win will not be the ones that use AI most during execution.
They will be the ones that use it at the earliest while decisions are still reversible.
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