When text becomes cheap, organisations start making decisions by accident.

That is the central governance risk of generative AI in everyday work. Not hallucinations. Not “bias” in the abstract. The more common failure mode is quieter: fluent drafts begin to function like commitments. The pause where judgement normally sits is compressed, displaced, or skipped entirely.

This is not primarily a technology problem. It is an operating problem.

Judgement is a decision, not a view

Judgement is what you do when there is no clean rulebook, the evidence is partial, and the consequences matter.

It has four features.

• Uncertainty is real, not cosmetic
• Trade-offs are unavoidable
• Reversibility is limited
• Accountability attaches to someone, whether you like it or not

That is why judgement cannot be reduced to “good writing” or “better thinking”. It is organisational work that needs roles, boundaries, artefacts, and review.

If you want to find judgement in the wild, look for moments where the organisation cannot simply “follow the process”, and somebody must own a choice.

The draft–decision collapse

Modern organisations already run on documents. Emails, policies, proposals, HR notes, risk assessments, customer communications. These texts do not merely record decisions. They often trigger action as if a decision has been made.

Generative AI accelerates this because it produces text that is fast, fluent, and socially credible. The fluency is the trap. A draft that reads well feels complete. That feeling is not evidence.

The result is what I will call the draft–decision collapse. The boundary between “we are drafting” and “we are deciding” erodes. A piece of text is circulated, and the organisation behaves as if commitment has occurred.

You can see this in small, expensive moments.

• A customer email includes a deadline, a refund, or an exception that nobody consciously authorised.
• HR notes drafted for “professional tone” drift into inference about motive, and later become evidence under subject access or dispute.
• A policy update makes a subtle shift in risk posture because the wording sounds reasonable.
• A procurement response contains unverified claims that then become part of the audit trail.
• An internal briefing becomes “the plan” because it looks coherent, even though it is assembled from fragments.

The AI did not decide. The organisation acted as if it had.

Speed now, rework later

The immediate benefit is throughput. The downstream cost arrives later, and it is distributed across teams.

Typical outcomes include:

• rework, because the exception is discovered after it has been implied in writing
• stress, because accountability lands on whoever clicked send
• reputational exposure, because polished language reads as official position
• legal and compliance risk, because records contain unauthorised commitments or loaded statements
• erosion of trust, because nobody can trace who approved what

This is why “productivity” can become a misleading metric in AI-assisted environments. Output rises, but coherence can fall. You get more text and less clarity about what is true, what is agreed, and who owns the consequences.

Decision rights are the core unit of governance

A lot of AI governance is principle-heavy and operationally thin. Principles matter, but organisations fail at the level of decision rights.

Decision rights answer questions such as:

• Who can commit money, deadlines, or service levels?
• Who can approve exceptions to policy?
• Who can accept risk on behalf of the organisation?
• Who can publish statements that external parties can treat as official?
• Who is accountable when this goes wrong?

Generative AI blurs decision rights because it enables decision-shaped text to be produced by people who do not hold authority to commit. The remedy is not panic, and it is not simply training. It is to rebuild the boundary between drafting and deciding into the workflow.

Evidence discipline, applied

Judgement quality is strongly linked to evidence discipline. What counts as “enough” evidence to justify commitment?

AI-assisted drafting increases the risk that evidential standards become implicit and inconsistent. The text looks finished before the claim is justified.

A practical way to restore evidence discipline is to make standards explicit in categories:

• Must verify: facts, figures, dates, prices, terms, references, compliance claims, clinical or legal assertions.
• Must attribute: sources, datasets, assumptions, and who supplied the information.
• Must log: rationale, uncertainty, trade-offs, dissent, and alternatives considered.
• Must escalate: exceptions, novel risks, reputational exposure, and anything difficult to reverse.

This is what “epistemic governance” looks like when you take it out of the seminar room and put it into operations. It is governance over how claims are formed, justified, and validated inside the organisation.

Without it, the organisation drifts into a substitution: it treats fluent text as evidence of adequate reasoning.

Judgement as an operating model

If judgement is organisational work, it belongs in the operating model. Not in a slide deck about values.

A workable operating model for judgement has a few visible components.

Tempo

Where do decisions actually happen?

• Which meetings and checkpoints function as decision points in practice?
• Where are exceptions approved, and by whom?
• What is the minimum pause required before a draft becomes a commitment?

Artefacts

What gets produced when a decision is made?

If the artefact is only a polished email, then decisions will be enacted without rationale, evidential trace, or clear ownership. Stronger artefacts include short decision notes, sign-off records, risk acceptances, and decision briefs that make the basis of commitment explicit.

Controls

Controls should be selective and proportionate. They should sit where irreversible cost lives.

Examples include pre-send review for customer communications with financial implications, second-person review for HR notes, approval gates for policy changes, and escalation triggers when language shifts from exploratory to committing.

The goal is not bureaucracy. The goal is to prevent unauthorised commitments wearing the disguise of professional prose.

Feedback and learning

Judgement improves when it is reviewed.

• Were the assumptions warranted at the time?
• Did the decision produce the intended effect?
• What was missed or mis-weighted?
• Should evidence thresholds change?

Without review, organisations do not learn. They repeat the same failure patterns at higher speed.

Make judgement visible

One of the most effective interventions is simply to make judgement visible and inspectable.

A judgement log is a lightweight record of:

• the decision being made
• options considered
• evidence available at the time
• uncertainties and risks
• who owned the decision
• what would change the decision
• when it will be reviewed

Used properly, this improves decision-making in the moment by forcing explicitness. It also improves learning later by reducing hindsight reconstruction.

There is an obvious failure mode. Logs can become performative, post-hoc justification. The difference is follow-through. A judgement log without review is just a nicely formatted opinion.

So the practice needs review tempo, scheduled points where judgements are confirmed, refined, or reversed, with explicit notes on what was learned.

The point

The risk is not bad AI. It is invisible decisions.

When drafting becomes effortless, organisations start committing to things without noticing, and then cannot trace who approved what. The response is not to ban tools or publish vague principles. It is to design operations so that drafting and deciding are separated, decision rights are explicit, evidence standards are operational, and judgements are recorded and reviewed.

Text is getting cheaper. Accountability is not.