Every new AI tool you license adds another custom connector, data mapping exercise and exception-handling layer that your existing teams must maintain. Most organisations treat this as a one-off project cost rather than permanent technical debt.
The pattern repeats across Melbourne and Sydney boardrooms: a flashy proof-of-concept runs on curated data, then stalls the moment it touches production records scattered across SAP, legacy mainframes and third-party portals.
Integration teams are already stretched maintaining decade-old interfaces. Adding AI simply multiplies the number of brittle hand-offs they must babysit, turning every promised efficiency gain into unplanned overtime.
Vendors rarely price or even disclose these downstream costs because their commercial model ends at model access. The result is a growing backlog of half-connected systems that deliver neither speed nor accuracy.
Boards keep approving fresh AI spend without demanding an integration architecture review first. This guarantees the next pilot will repeat the same expensive discovery phase twelve months later.
Fixing it requires shifting ownership of integration outcomes to the business units that actually depend on the data flows, not leaving it with central IT or external integrators who have no incentive to simplify.
Until that accountability moves, AI budgets will continue funding more connectors instead of fewer, more durable connections.