Marketing Already Knows How to Solve Its AI Readiness Problem. It’s Called a Close Process.

Marketing keeps describing its AI readiness problem as a data problem: messy sources, no single source of truth, models fed on numbers nobody fully trusts. Supermetrics’ new AI Readiness Gap report backs that up: 85% of the 435 marketing leaders surveyed have no formal AI strategy or clear ownership of one, and only 11% call their data extremely high quality and consistently accessible. 

But I’d push back on that being the final diagnosis. What looks like a data problem is really an accountability problem that’s been hiding behind data language for years, and AI is the first thing forcing that distinction into the open. 

Finance faced this exact problem decades ago and solved it with something unglamorous: the close process. Every month, finance reconciles numbers from different systems, assigns an owner to every discrepancy, and doesn’t report a figure until someone has signed off on where it came from and why it’s right. Nobody in finance would let a model post to the ledger without that structure. Marketing routinely lets AI act on unreconciled data, because it’s never built the equivalent of a close. 

Where marketing loses the thread 

The place this shows up most clearly is the conflation of demand creation and demand capture. Teams build dashboards and AI tools that treat “a lead came in” as the unit of success, without distinguishing between demand the team generated and demand that simply showed up and got attributed to the nearest channel. The report’s numbers on the “last mile” are really evidence of this: 40% of small and midsized teams and 34% of enterprise teams cite weak connections between analytics and activation as their biggest blocker, and under a third of ad-hoc data requests get answered in real time. Those numbers point less to broken pipes than to a metric nobody agreed on before someone automated the reporting on it. 

Feed an AI system a funnel that already blurs creation and capture, and it will optimize against that blur faster and with more apparent confidence than a person would, all without ever flagging it as a problem. That’s the real risk in the report’s data: 80% of marketers feel pressure to adopt AI while only 6% say it’s fully integrated into their workflow. The gap between those two numbers is exactly where an unreconciled metric gets automated before anyone catches it. 

Why “who owns the data” is the wrong first question 

Most AI readiness conversations start by asking who owns the data. In finance, that question comes second. Finance starts by asking who owns the decision: who signs off when a number looks wrong, who is accountable if it’s used anyway, and who has the authority to say “not yet.” Only after that’s answered does the data-ownership conversation become tractable. 

This is where a DACI structure (Driver, Approver, Contributor, Informed) earns its keep, and where most marketing organizations quietly skip a step. Naming a data owner is the easy part. Naming who holds Approver authority the moment an AI recommendation touches real budget, and what happens when that person isn’t in the room, takes more discipline. Without that answer, “AI readiness” becomes a euphemism for turning on a tool and hoping the org chart sorts itself out afterward. 

Give AI outputs the same discipline as a monthly close 

If marketing borrowed finance’s close discipline, it would look like this: before any AI system is allowed to influence a decision above a certain threshold, someone reconciles the inputs, someone approves the output, and the approval is logged the same way a journal entry is. That discipline has nothing to do with the bureaucracy of accounting and everything to do with separating a number you can act on from a number that merely arrived quickly. 

Practically, that starts with naming an Approver for the handful of decisions AI is actually influencing (channel budget shifts, segment prioritization, campaign kill/continue calls) before the model touches them. It means splitting every reported metric into “created” or “captured” before it reaches a dashboard, because a metric that can’t be cleanly assigned to one is unreconciled by definition. And it means logging AI-influenced decisions the way finance logs adjustments, building a trail so that six months from now, someone can trace why a budget moved and who signed off on it. 

Access was never the differentiator 

Every company will soon have access to comparable AI models at a comparable price. That was true of ERP systems, too, and it didn’t make every finance function equally trustworthy. What separated them was discipline: consistent definitions, named approvers, and a process that closes the books before anyone reports the number. 

Marketing’s AI readiness gap will close through the unglamorous decision to name an owner, define what’s being measured, and refuse to let a system report a number nobody would sign their name to. No bigger model or cleaner dashboard gets there faster.