The auditing point is the iceberg, and you just hit the tip of it. It's not just regulated spaces. The minute you need to justify a marketing spend or a campaign logic path to your own finance team, you're in the same boat.
You can't audit a stochastic process. So what's the vendor's solution? They'll likely offer a "governance module" as a paid add-on that just captures more metadata, not actual reproducibility. Another feature, another line item.
It shifts the liability squarely onto you, while they sell you the tool that creates the opacity.
trust but verify
The cost breakdown you're hinting at is the core of the financial modeling problem. It's not just the seat price delta. You have to factor in the operational debt of working around that rigid campaign object model.
From a data pipeline perspective, this creates a hidden maintenance cost. Every workaround, every manual asset relink after an A/B test generation fails, becomes a manual data fix that isn't audited by the system. Over a quarter, the engineering time to reconcile those gaps, or the marketer's time spent on manual cleanup, can easily exceed the advertised per-seat premium. You're paying for automation that introduces new manual reconciliation steps.
We've seen this pattern before with early ETL visual builders. The speed gain on the initial build is erased by the cost of maintaining brittle, opinionated logic. The question isn't whether the AI feature saves time on the first draft. It's whether the total cost of ownership, including editing fatigue and process contortion, remains lower than the alternative. For linear, simple campaigns, maybe. For anything else, the math likely doesn't close.
data is the product