Most Businesses Don’t Have a Performance Problem; They Have a Decision-Making Problem

As I noted in my previous article, the conversation that “more data doesn’t mean better leadership” is now out in the open, which naturally leads to the next question.

If data doesn’t make decisions, what does?

The answer is uncomfortable for some leaders. Judgment. Context. Accountability. And the systems that support them.

In other words, decision-making quality is not a personality trait of leadership. It’s a business capability.

Decisions are the real output of leadership

Most organizations measure activity. Some measure outcomes. Very few intentionally align to decision quality.

Yet when you step back, decisions are the real output of leadership. Every strategy becomes a series of decisions. Every KPI review is really a decision forum. Every meeting either advances decisions or delays them.

If decisions are slow, inconsistent, or reversible, performance suffers, no matter how good the strategy looks on paper.

This is why decision quality belongs squarely in operations, not just in leadership theory.

Why good people make bad decisions at work

When leaders struggle with decisions, it’s rarely because they lack intelligence or experience. It’s usually because the environment works against them.

A few common culprits recur.

Unclear decision rights. When it’s unclear who owns a decision, it either gets made by committee or not at all.

Poor framing. Decisions are presented as binary choices when they aren’t, or as tactical issues when they’re actually strategic.

Misaligned incentives. People optimize for their function, their metric, or their own risk exposure, not the organization’s goal.

And finally, noise. Too much data, too many opinions, too many voices, all arriving at the same time.

None of these is a personal failing. They are design flaws.

Decision quality can be designed

This is where operations come in.

High-performing organizations don’t just hope for good decisions. They design the conditions for them.

They are explicit about what decisions matter most. Not every decision deserves the same level of rigour. Some are reversible. Some are not. Treating them all the same creates drag (yes, back to some of my #PhysicsFriday topics).

They clarify who decides, who contributes, and who is informed. This alone removes enormous friction.

They build decision cadence into the operating rhythm. Certain decisions happen weekly. Others are monthly or quarterly. This prevents both knee-jerk reactions and endless deferral.

And they link decisions to outcomes. When decisions are reviewed not just for results but for quality of thinking, learning accelerates.

This is not bureaucracy. It’s focus.

Data supports decisions, but it doesn’t define them

This is where the data conversation matters.

Good decision environments use data as an input, not a verdict. The question is not “what does the data say” but “what does the data suggest, and what are we choosing to do about it.”

Strong leaders are comfortable making a call when the data is incomplete but directionally clear. Weak systems force leaders to wait for certainty that never arrives.

Decision quality improves when leaders are expected to explain their reasoning rather than point to numbers.

Meetings are decision factories, whether you like it or not

One of the most overlooked operational levers is the meeting structure.

Most leadership meetings are framed as updates. In reality, they are decision factories. Or at least they should be.

If a meeting does not exist to make, shape, or validate decisions, it should be questioned.

Updates can be asynchronous. Decisions cannot.

Clear agendas that specify expected decisions, required inputs, and constraints dramatically improve decision quality.

So does ending meetings with explicit decisions and next actions, rather than vague alignment.

The role of leadership judgment

This is the part that can’t be automated.

No system, framework, or dashboard replaces judgment. It only sharpens it.

Good judgment is contextual. It weighs data, experience, risk, and timing. It understands that the same decision can be right in one context and wrong in another.

Organizations that value decision quality create space for this. They don’t punish thoughtful decisions that lead to poor outcomes. They do challenge poor thinking, even when outcomes are good.

That distinction matters.

Measuring decision quality without killing it

The idea of measuring decision quality makes some leaders uncomfortable. And it should, if done poorly.

This is not about scoring decisions like a test.

It’s about asking better questions after the fact. Call it the post-mortem or lessons-learned review, as in project management.  Were assumptions explicit? Were trade-offs understood? Were alternatives considered? Was ownership clear?

Over time, patterns emerge. Certain teams make clearer decisions. Certain forums produce better outcomes. Certain decisions repeatedly cause rework.

That’s operational insight, not micromanagement.

Why this matters more as organizations grow

As organizations scale, the number of decisions increases faster than headcount. Complexity compounds. Informal alignment breaks down.

What worked when everyone sat in the same room no longer works.

This is usually when leaders feel things are slowing down, even though everyone is working harder. The issue is not effort. It’s decision friction.

Treating decision quality as an operational capability is how organizations regain momentum without burning people out.

Final thought

Strong leadership is not about having all the answers. It’s about creating the conditions that enable consistent good decisions.

Data helps. Experience helps. But without structure, clarity, and accountability, decision quality remains accidental.

And accidental decisions are a risky way to run a business.

Design the business operating system, and better decisions follow.