Moving AI from pilot to production starts with the data

AI adoption has moved quickly.

McKinsey’s 2025 global survey found that 88% of respondents say their organizations regularly use AI tools in at least one business function, up from 78% the previous year, yet only 7% say AI is fully scaled across the organization, with 30% remaining stuck in the piloting phase.

For marketing teams, this gap becomes even more obvious when an AI pilot meets the complexity of the existing data stack.

A pilot can demonstrate what an AI model is capable of when it has a defined task and carefully selected data, but moving that capability into a live enterprise environment is often considerably harder.

The issue often sits beneath the model itself.

Marketing data is spread across platforms, warehouses and internal systems, with different structures and definitions making it difficult for AI to establish what the numbers actually mean.

The pilot works until it meets the real world There is a reason AI pilots can look so convincing at the beginning.

The scope is usually narrow, the data is easier to control and the questions being asked are relatively straightforward.

Production however introduces a new and different level of complexity.

An AI system working across an enterprise marketing stack for example may encounter several versions of the same metric, with different field names and different rules for how they should be calculated.

Without a governed understanding of that environment, the model can act like a black box and make its own assumptions.

Cost is a simple example.

One platform may store it under one field name while another uses something completely different.

To a human who understands the organization's data, the distinction may be obvious, but to an AI model operating without that knowledge layer, the first plausible match can look like the right answer.