Not code-related, but relevant if you're a technical lead who ever has to justify tooling/infrastructure spend to finance — the same logic applies almost 1:1.
The common mistake: teams price the new system first, then reverse-engineer benefits large enough to justify it. A better model starts by quantifying the cost of the current fragmented state — duplicate data entry, manual reconciliation, reporting lag, integration gaps. Sound familiar? It's basically "tech debt cost modeling" applied to ERP.
Key structural point that transfers well to engineering budget conversations: separate initial cost, recurring cost, and benefit into distinct buckets. When they blur together, any payback-period number becomes trivially easy to manipulate (intentionally or not) — same failure mode as conflating one-time migration cost with ongoing maintenance savings in an infra proposal.
Also relevant: don't count "mature-state" gains as day-one wins. Adoption ramps take time — same as expecting full productivity from a new internal tool the week it ships.
The most interesting stat: a large share of orgs report using AI in at least one function, but only about a third have actually scaled it — the gap between "adopted a tool" and "changed the process around it" being where most claimed ROI quietly dies.
Full piece: https://theintechgroup.com/blog/odoo-erp-roi-guide/
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