AI governance in agencies: who validates what the machine produces?

The question is no longer whether an agency uses AI, but who, precisely, validates what it produces before it reaches the client. In the majority of agencies I observe, this question has no formalised answer. That is not a problem of bad intentions - it is a process blind spot, and it always materialises at the worst moment.

The most common ambiguity

Many teams use AI tools individually, without a formalised review process: everyone does their own verification, with their own criteria, with no traceability. The result is variable quality depending on the person, the day, and the project - and no visibility on what actually goes out to the client.

A concrete case that comes up regularly

A copywriter generates a blog article with AI, reads it quickly, and delivers it to the client. The article contains a factually incorrect statement about current regulations. The client publishes it, a prospect flags it. The question the client inevitably asks: "who approved this?". If the answer is "we used AI and it was reviewed", that is not sufficient - and in some contexts (healthcare, finance, law), it can have serious consequences.

The most frequent grey areas

High-risk deliverables are not always the ones you would expect. AI-generated meeting minutes sent without review, partially reformulated contract drafts, legal or regulatory monitoring summaries produced in seconds - these are often deliverables that go out fast, without the level of validation they deserve. The sense of time saved creates an implicit pressure to accelerate review, sometimes to the point of eliminating it.

A minimal governance to put in place

AI governance in an agency does not need to be complex to be effective. Three decisions are enough to cover the essentials.

Which deliverables go into which category

The first decision is a classification of deliverables: those that can integrate AI output with light review (formatting, internal summaries), and those that require expert validation before delivery (factual content, contractual documents, regulatory advice). This list is not long - it fits on half a page. But it must be explicit and shared with the whole team, not left to each person's discretion.

How to document without adding overhead

Traceability does not mean a checkbox for every generated sentence. It means being able to answer "who reviewed and validated this deliverable?" for every AI output sent to a client. In practice: a field in the brief or purchase order, a note in the internal email thread, or a file naming convention. It is not heavy - it is the minimum to be able to answer the question if it is raised.

1. Explicitly define which deliverables can integrate raw AI output, and which require systematic review
2. Designate clear responsibility for final validation, not a diffuse verification where everyone assumes someone else did it
3. Track cases where AI output was corrected, to identify recurring high-risk uses and adjust processes accordingly

Why this is not just more overhead

Without this governance, it is client trust that ends up paying the bill for the first incident: incorrect information, an inappropriate tone, generic content delivered as personalised. The cost of such an incident - in correction hours, client crisis management, reputation - far exceeds the cost of setting up a proper validation process.

AI governance is not an additional administrative constraint. It is the condition for the time savings promised by AI to be real and lasting, rather than a short-term economy that backfires at the first serious incident.

An agency that cannot clearly answer "who approved this?" is not ready to generalise AI in its production. That is not a criticism - it is a diagnosis. And a diagnosis is something you treat.