Shifting the debate from High-Level vs Detailed Planning Debate in the age of AI

R. Quaine
Dec 17, 2025
2 min read

Updated: 5 days ago

Ending the High-Level vs Detailed Planning Debate

For years, organisations have been asked to choose between two imperfect options.

Plan at a high level to stay agile.
Or plan in detail to improve control and execution.

Most organisations alternate between the two, simplifying plans to regain speed, then reintroducing detail when execution weakens. The result is slow cycles, frustrated managers, and declining confidence in the numbers.

The real issue is not the amount of detail. It is how that detail is created.

The false trade-off

High-level, driver-based planning has become the default response to volatility. Rolling forecasts and outcome-focused targets reduce cost and improve responsiveness.

But when plans stop at the aggregate level, strategy often fails to translate into action. Variances are harder to explain, accountability weakens, and execution drifts.

The instinctive fix is to push planning back into manual detail, impacting agility.

Why manual detail no longer works

Traditional bottom-up planning scales poorly:

Cycles slow as coordination increases
Finance effort shifts from insight to data collection
Plans become expensive to change and adapt

The irony is clear: detail intended to improve control ends up reducing speed, trust, and decision quality.

Detail is not the problem. Manually produced detail is.

A better planning question

Leading CFOs are reframing the debate.

No longer “How much detail do we need?”
But “What detail provides value, where can the system generate it, and when should humans apply judgement?”

That shift changes everything.

From entered numbers to derived insight

Modern planning models now treat top-down, driver-based planning with standards and allocation to detail as current best practice.

Strategic targets set direction.
Drivers and cost standards translate intent.
Systems generate operational detail where it is needed.

Granularity is no longer negotiated or typed in line by line. It is derived consistently, transparently, and at scale.

Where AI extends the model

Human context still matters. Known events, emerging risks, and local insight cannot be fully automated.

This is where conversational AI and intelligent agents add value by extending the capacity of finance teams. They capture judgement through structured dialogue with stakeholders and translate it directly into planning system data for finance review.

The outcome is better information, faster updates, and less manual effort.

What this means for CFOs

The most effective finance leaders are no longer choosing between high-level and detailed planning.

They are designing models where: