Writing an AI Marketing Policy People Will Actually Follow
Most internal AI policies are written to satisfy a risk committee and are then ignored by everyone who has actual work to ship.
Artificial intelligence moves marketing away from producing individual assets and towards designing, supervising and correcting the systems that produce them at volume.
For most of the past two decades an Australian marketing team was measured by what it produced. Campaigns shipped, pages published, emails sent, decks presented. The unit of work was the asset, and the structure of the team was built to move assets through a queue of specialists. Artificial intelligence does not make that queue faster so much as it makes the queue the wrong shape.
When a competent model can draft twenty versions of a landing page in the time a copywriter takes to draft one, the constraint moves. It stops being production capacity and becomes judgement: which versions are worth having, which are on brand, which are true, which will survive contact with a customer. A team that keeps organising itself around production will simply produce more of what nobody asked for.
A systems team designs the machinery that produces work, then supervises what comes out of it. That sounds abstract until you write down the artefacts it owns. They are unglamorous, and they are the job.
None of this is exotic. It is the discipline that software and manufacturing teams adopted long ago, applied to a function that never needed it, because a human bottleneck was doing the quality control invisibly.
Three roles change quickly. The content producer becomes an editor with a much larger surface to cover, which makes the skill triage rather than craft applied evenly to every piece. The channel specialist becomes a systems owner, responsible for the whole loop of brief, generation, review and measurement in that channel. The marketing operations lead, previously a background function, moves to the centre, because the systems are now the product.
The role that changes least is the one holding customer understanding. If anything it becomes scarcer and more valuable, because the volume of plausible output rises far faster than the supply of people who can tell whether it is right.
Weekly status meetings that read out asset progress stop being useful when assets are cheap. Replace them with a review of system behaviour: what did the pipeline produce this week, what did we reject, why, and what does that tell us to change upstream. Rejection reasons are the most valuable data a systems team generates, and almost nobody records them.
Then add one ritual that does not exist in most teams: a scheduled read of raw output before anyone edits it. Ten minutes looking at what the system actually produces, unimproved, will tell you more about its drift than any dashboard.
A marketing team that cannot describe its own quality standard in writing has no way to supervise a machine that is usually, but not always, right.
The most common failure is not a bad model. It is a good draft handed to someone who does not know what has already been checked. The reviewer assumes the facts were verified. The writer assumed the reviewer would verify them. The claim goes out with nobody standing behind it.
Fix it with a small habit: every hand-off carries a note of what was checked and what was not. It takes a sentence. It is the cheapest governance any team will ever put in place, and it survives staff turnover in a way that tribal knowledge does not.
Systems thinking has a reputation for being slow and bureaucratic. Done properly it is the opposite. It moves the argument about quality to the front of the process, once, instead of relitigating it on every asset for the rest of the year.
Published by the Australian Centre for AI in Marketing
Free to read, free to share, and free of any vendor interest.
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