Every week another vendor lands in Melbourne promising an AI layer that will 'unlock value' from your existing stack. The demos look clean. The reality is your customer records live in three ERPs, two CRMs and a spreadsheet maintained by finance.
Data fragmentation is not a minor hygiene issue. It is the primary reason most AI deployments plateau at proof-of-concept. Models trained on incomplete or conflicting inputs simply amplify the noise at scale.
The usual response is to hire a data governance committee. That committee produces a policy document, then hands the integration problem back to the same underfunded IT team that has been firefighting legacy issues since 2018.
Integration tax is rarely modelled in the original business case. Yet every new AI use case quietly adds another set of APIs, reconciliation scripts and exception-handling processes that someone has to maintain forever.
Boards keep approving AI spend because the narrative stays focused on model capability rather than data plumbing. Until the operating model makes data ownership a line responsibility with real consequences, the pattern repeats.
The organisations pulling ahead are not those with the fanciest models. They are the ones that have forced product and operations owners to jointly sign off on canonical data definitions before any model touches production.