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When established consolidation vendors add "AI features" to their platforms, the demos are compelling. Automated summaries. Intelligent variance explanations. Copilot-assisted month-endreview.
The problem is not the AI. The problem is what the AI is working with.
Legacy consolidation platforms were designed around periodic processing. You collect data at month-end, run the consolidation, produce reports. That was the right architecture for the era in which they were built. But it means that by the time the AI layer gets to work, the data it operateson is already four, six, sometimes eight weeks old — batch-processed, manually reconciled, and riddled with the errors and gaps that manual processes inevitably introduce.
An AI that summarises stale data does not solve the timing problem. It produces more sophisticated reports of information that is already too old to act on. The covenant that could have been caught early is stilldiscovered at month-end. The operational issue that surfaced in week one isstill invisible until week four. The acquisition that needed monitoring fromday one still goes unmonitored for months.
It is a consequence of the architecture underneath it. Bolt-on AI inherits the constraints of the system it sits on top of. If data arrives in batches, the AIworks in batches. If intercompany reconciliation is a manual month-end process, AI-assisted reconciliation is still a month-end process.
The analogy that resonates: adding a turbo charger to a bicycle. The underlying design constrains what is possible, regardless of how sophisticated the enhancement.
Genuine AI-native architecture makes different assumptions at the foundation. Data flows continuously from source ERPs. Account mapping runs at ingestion, not at month-end. Intercompany patterns are identified as transactions occur, not after the fact. The AI is not a feature added to the consolidation process — it is how the consolidation process works.
That distinction — native versus bolt-on — is what separates a platform that changes the timing problem from one that makes the existing process marginally more efficient.
[Why AI-Native Architecture Is the Only Way to Automate Intercompany Eliminations Across Multiple ERPs]*
