Field notes · Topic
Operations & ERP.
Back-office work is full of documents to read, records to match and exceptions to explain. These notes show where AI helps inside ERP-centered workflows, and why numbers and postings stay with your systems and your people.
8 notes
Notes on this topic
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Invoice automation in Türkiye: where e-Fatura ends and AI begins.
Structured e-invoices already arrive as data. The hard part of invoice automation in Türkiye is everything else: foreign supplier PDFs, scans, delivery notes, credit notes and matching to orders. That is where AI helps, with an accountant approving every entry.
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AI for finance teams: from invoices to month-end, where to start.
Finance work is full of reading, matching and explaining, which suits AI, and full of numbers that must be exact, which doesn’t. Let systems compute and reconcile, let AI read and draft, and keep approvals with the people who sign.
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Four ways to connect AI to your ERP, and the trade-offs of each.
There are four practical ways in: the ERP’s own APIs, read-only database views, scheduled exports, and middleware or screen automation as a last resort. Choose per workflow by freshness, safety and upkeep, and start read-only.
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Management reports with AI: numbers from systems, words from the model.
Never let a language model calculate the numbers in a management report. Compute figures in the ERP or BI layer, let the model draft commentary from them and the notes people already write, and cite every statement to its figure.
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What language models add to demand planning, and what they don’t.
Forecasts are a job for statistical and machine learning models on sales history. Language models help around them: reading supplier and customer messages for signals, explaining forecast changes in plain words and answering planners’ questions with sources.
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AI for procurement: spend, supplier documents and quote comparison.
Procurement is heavy on documents and on judgment. AI can classify spend, read supplier documents and quotes, and compare offers line by line with sources; the choice of supplier and every commitment stay with buyers.
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Logistics documents and AI: what to automate first.
Logistics runs on documents from many parties, in many formats and languages. Start where a document triggers a decision, such as a proof of delivery that releases an invoice, extract with evidence and route exceptions to the right desk.
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Cleaning supplier and material master data with AI, safely.
Duplicate suppliers and inconsistent material descriptions quietly break matching, reporting and every AI workflow built on top. AI can propose merges and standard descriptions with evidence; data owners approve each change, and nothing merges automatically.