The Problem

AI and automation are now being used to make real decisions.

These decisions affect:

• people

• money

• access

• risk
  
  

They are based on data.

If the data is incomplete, duplicated, poorly defined, or misunderstood, the decision will reflect that. Not because the system is careless, but because it has no way to know otherwise.

Most organisations assume their data is “good enough” because it works operationally. That assumption used to be reasonable. It no longer is.

What Has Changed

Before AI, data was mainly used to support people.

Today, data is increasingly used to replace judgement.

Once a system is automated:

• errors scale

• assumptions harden into outcomes

• explanations become difficult after the fact
  
  

When questions are asked later, organisations are expected to show that the information used was appropriate at the time the decision was made.

Many cannot.

Where the Risk Comes From

The risk does not usually come from bad intent.

It comes from ordinary conditions:

• data collected for one purpose being reused for another

• datasets combined without clear lineage

• duplicates and gaps that go unnoticed

• definitions that differ across systems
  
  

These issues are common, understandable, and often invisible until something goes wrong.

At that point, it is too late to discover them.

Why Improvement Is Not Enough

Most existing approaches focus on improving data over time.

That is useful work, but it does not answer a simple question that now matters:

Is this data acceptable to use for AI-driven decisions today?

Improvement plans, roadmaps, and best practices do not provide that answer. They describe intent, not condition.

When decisions must be justified, intent is not sufficient.

The Gap

There is a gap between:

• data that functions operationally
  and

• data that can be safely relied on for automated decisions

That gap is rarely measured clearly.

This company exists to make that gap visible.