Why Macros and Chatbots Both Fail at Reconciliation
IT departments have spent the last decade trying to automate reconciliation, and the attempts stall in one of two predictable ways. Understanding both is the fastest path to the design that actually works, covered later in this series.
The fragility of macros and RPA
Traditional automation, macros and robotic process automation alike, demands highly predictable data. It executes rigid, rules-based logic that has no room to bend.
The moment a vendor adds a new column to an invoice, or a department head submits a status report as an unformatted PDF instead of the standard template, the script breaks. Not degrades gracefully. Breaks. Legacy automation was built for a world where the input never changes shape, and enterprise data never stops changing shape.
This isn't a criticism of the engineering. Rigid logic is precise by design, which is exactly why it can't tolerate the unstructured reality most operations teams actually live in.
The uncontrolled AI chatbot problem
The current, understandable instinct is to swing the opposite direction: upload a massive spreadsheet into a general AI chatbot and ask it to find the discrepancies.
This introduces a different kind of risk, and it's a real one, not a hypothetical. Asked to reconcile large datasets in a single prompt, general-purpose AI models can produce omissions, inconsistent outputs between runs, and associations between rows that look plausible but aren't actually supported by the data. A casual prompt in a chat interface is not a reconciliation system, no matter how capable the underlying model is. Complex reconciliation needs a tightly controlled process, and a chat window is the opposite of that.
Why both failures point at the same fix
Look at these two failure modes side by side and the shape of the actual solution becomes visible. Rigid automation fails because it has no judgment. Uncontrolled AI fails because it has too much unstructured freedom.
The fix isn't picking one over the other. It's using each where it's actually good: AI for the parts that require semantic judgment, deterministic logic for the parts that require certainty. Where exactly that line falls is a decision worth making explicitly, workflow by workflow, rather than assuming one tool handles everything.
That decision framework, the specific test for which parts of a reconciliation problem are genuinely AI-shaped and which aren't, is the next piece in this series.
Adapted from the full white paper on designing AI reconciliation systems. Download the PDF.
Rosemarie Withee has spent thirteen years helping operations teams get real work out of their software, first Microsoft 365, now AI. She's written six books for Wiley and builds AI products at Portal Integrators.