To verify what reached your ERP, you need two records: what the operational layer sent, and whether the automation that sent it actually ran. Why silent failures matter here The reason to verify what reached your ERP is timing: a missing entry is discovered at closing, when correcting it is
To handle instalments in an approval process, decide what is being approved: the total commitment, or each payment. The answer determines how the records are structured. Why instalments cause confusion One commitment produces several payments, and most forms assume one of each. * Total value approved but individual payments untracked * Each
To map expense categories for your ERP, the request form has to collect exactly what the accounting entry requires. A field missed at entry becomes a manual step at posting. Why entries fail to post cleanly Teams that fail to map expense categories for your ERP usually designed the request
Deciding how to handle a request that needs correcting after approval matters because the instinct — editing the record quietly — destroys the audit trail that made approval meaningful. What goes wrong with quiet edits The record ends up describing something nobody approved. * A value changed after approval, with the approval still
To organize receivables follow-up, the chasing has to be a process rather than a habit. Collections that depend on someone remembering are collections that happen late. Why follow-up slips It is nobody's primary responsibility and everybody's secondary one. * Overdue invoices noticed during monthly review
To handle recurring payables, you need a middle ground between approving the same expense monthly and letting it renew unexamined for years. The two failure modes Both are common and both are avoidable. * Every recurring payment re-approved manually, wasting the approver's attention * Recurring payments never reviewed, renewing
Deciding how to tell customers you use an AI agent is worth settling deliberately. Transparency handled well costs nothing; handled badly, or discovered, it costs trust. What customers actually object to Rarely the technology itself. * Being unable to reach a person when they need one * Repeating themselves after a handover
To keep an AI agent's knowledge current, someone has to own the documents it answers from. An agent repeats what it was given, including things that stopped being true. How knowledge goes stale The agent works, so nobody revisits it. * A policy changed and the document not updated
Being clear about what AI cannot do in operations is what makes the rest credible. An agent is capable within a defined scope and unhelpful outside it. Where it does not help None of these are temporary limitations of a particular model. * Deciding what your process should be — that requires
To expand AI to another process, the first one has to have produced evidence. Expansion based on enthusiasm rather than measurement is how a second deployment ends up worse than the first. When you are ready Four conditions, all measurable. * The first agent handles a meaningful share of its process
To use AI for classification safely, separate describing a record from deciding what happens to it. An agent can categorize well while every consequential decision stays with a person. Why classification is the safest starting point The fields involved are high-volume, pattern-following, and cheap to correct. * Category left
To combine an AI agent with human handover, the transition has to be invisible to the customer. A handover that restarts the conversation undoes most of the value the agent created. What makes handover feel bad The customer experiences the seam, not the technology. * Being asked to repeat what they