Starts with
AI influence
Even when AI output looks finished, teams still need a clear way to judge the right level of review.
FMD Signal helps teams decide how much review AI-assisted work actually needs.
AI-assisted output can look finished before it has been properly checked.
That creates a common problem: people may trust it too quickly, and teams are often unclear on what level of review is actually required.
Signal helps make that clearer. It gives teams a more practical way to match review effort to risk, so checking is more consistent and more proportionate.
Clearer review decisions
Proportionate governance
Checks
Data and relianceOutputs
Review expectationAI influence
InputData sensitivity
ContextUse
RelianceSignal
OutputHow Signal Works
See how a team would use Signal in practice.
Move through the judgements a team would make before relying on AI-assisted work.
Step 1
How much did AI shape the substance of the output?
From light drafting support to work that shaped meaning, options, or recommendations, start by judging the level of AI influence.
Step 2
What is the highest-sensitivity information involved?
Personal, confidential, sensitive, or restricted information should raise the level of care even if the rest of the work is more routine.
Step 3
What will this output actually be relied on for?
A rough draft, an internal summary, and decision support may look similar, but they do not call for the same review response.
Step 4
What review expectation does Signal return?
Signal brings those earlier judgements together and returns the review expectation: what level of checking, ownership, and evidence is needed before reliance.
The Problem
AI output often looks more dependable than it is.
That polished finish changes behaviour. People trust outputs too quickly, review varies from person to person, and similar cases get handled differently.
01
Finished-looking output attracts over-trust.
Fluent language and tidy formatting can make weak work feel ready before it deserves that confidence.
02
Review becomes inconsistent.
One person treats it as a rough draft. Another treats it as decision support. The control response drifts.
03
Teams over-check some work and under-check other work.
The result is unnecessary friction in some places and weak assurance where it matters more.
The Idea
Match review effort to risk instead of treating every AI use the same.
Signal makes the judgement clearer. It gives teams enough structure to decide what needs light review, what needs stronger checking, and why.
Signal helps teams ask
- How much did AI shape this output?
- What kind of information was involved?
- What will this output be used for?
- What level of review should that trigger?
Working rule
More AI influence, more sensitive information, and heavier reliance should lead to stronger review.
Four Dimensions
The four judgements behind the Signal output.
AI influence
How much did AI shape the substance of the output, not just the wording?
Data sensitivity
What is the highest-sensitivity information involved, not the average?
Use
What will the output actually be relied on for, not just what format it takes?
Signal
What level of review and control should this trigger?
Simple Example
Not every AI-assisted output needs the same review response.
Same organisation. Different use. Different review response.
Worked example
Meeting summary for internal planning
AI helps draft a summary for an internal team update. The output supports planning, but it is not the final basis for a regulated or high-consequence decision.
AI role
Light drafting helpData
Routine internal informationUse
Internal planningSignal
Proportionate review before sharingThe review is still real, but proportionate.
Signal output
Returned review expectation
Starting point
Drafting support creates a baseline need for human reviewRaised because
No further uplift is needed beyond normal internal planning useSignal
Human review before sharingThe Outcome
What clearer review looks like in practice.
Clearer review expectations
Teams can explain what needs a light check, what needs stronger review, and why.
More consistent handling
Similar cases are treated more consistently instead of depending on personal habit.
Proportionate control
Review effort rises where AI influence, sensitivity, or reliance is higher.
Stronger ownership
Higher-risk use is easier to assign, review, and justify.
Better evidence of checking
Where stronger review is needed, the expectation for validation and record-keeping is clearer.
Less friction in the wrong places
Teams spend less time over-checking low-risk work and under-checking what matters more.