AI-powered process automation

How is this different from classic automation?

Classic automation runs on rules: “the invoice total is in this field, write it to that table.” It is reliable as long as the input format holds. When the format changes, it stops.

In most companies the real workload sits where rules cannot reach. Customer requests arrive as free-text emails or chat messages. Supplier invoices are PDFs laid out differently by every vendor. A quote request is a note from a phone call. For years these steps waited on someone’s desk.

Language models close that gap. A model can read an email and decide what kind of request it is, pull the amount and tax ID out of a PDF, or draft a reply. The rest of the workflow still runs on classic automation tools such as n8n, Make or Power Automate.

Where should AI sit in the workflow?

Step type	Example	Best-fit tool	Human approval
Rule-based	Writing order data into the ERP	n8n, Make, API integration	Usually not needed
Screen-based	Data entry into a legacy app with no API	RPA (UiPath, Power Automate)	On exceptions
Understanding and classifying text	Routing inbound email to a team	Language model	On low-confidence results
Extracting fields from documents	Amount and tax ID from a supplier PDF	Language model or document understanding service	Above an amount threshold
Drafting	Customer reply, quote summary	Language model	Always, before sending
Open-ended work	Multi-source research, complex support case	AI agent	Defined limits and checkpoints

The principle behind the table: use a model only where a step needs one. Anthropic’s essay Building effective agents makes the same point. It separates “workflows”, which follow predefined paths, from “agents”, which choose their own steps, and recommends starting with the simplest solution and adding complexity only when it is needed.

Which process should you start with?

The first process sets the credibility of everything after it. Pick one where all four answers are yes:

• Frequent? Does it repeat several times a week or more?
• Bounded? Can you describe its input and expected output?
• Costly when wrong? Does a mistake or delay cost money or customers?
• Reachable data? Can the systems involved be reached by API, database or, at worst, the screen?

Typical first candidates: supplier invoices flowing into accounting, marketplace or web-shop orders posted to the ERP, inbound email and chat requests classified and assigned, and weekly sales reports. For companies operating in Türkiye, e-invoices (e-Fatura and e-Arşiv) and local ERPs such as Logo, Mikro and Netsis are part of the same picture.

Where should people stay in the loop?

A workflow is trustworthy to the extent that you know where people step in. We recommend approval steps in three places.

First, anything that leaves the company. A model can write the message to a customer, supplier or authority, but a person sees it before it is sent. Second, money movement. Payments or refunds above a set amount wait for approval even if they were prepared automatically. Third, low-confidence results. When the model is unsure, the item goes to a queue and an employee decides.

Tools such as n8n make it straightforward to place these approvals inside the workflow. The approval can be an email button, a Slack or Teams message or a simple panel. What matters is that who approved what is recorded.

How do you manage the risks?

A workflow that calls a language model carries risks classic automation does not: wrong fields, invented details, different answers to the same input at different times. NIST’s AI Risk Management Framework organises the work into four functions: govern, map, measure and manage. At SME scale that translates into a few habits:

• Log the input and output of every model call.
• Ask the model for structured output against a schema and reject anything that does not match.
• Keep a small test set built from real cases and rerun it whenever the model or prompt changes.
• Strip unnecessary personal data before it reaches the model.

Regulation matters too. GDPR governs personal data you send to a provider. The EU AI Act (Regulation (EU) 2024/1689) adds obligations that depend on the use case, from transparency when people interact with an AI system to stricter rules for high-risk uses. If you process data of people in Türkiye, Türkiye’s data protection law (KVKK, Law No. 6698) applies and places conditions on transfers abroad. Settle the legal basis and provider settings with your legal counsel before go-live.

How do you measure the result?

We give no percentages here, because other companies’ numbers say nothing about your process. Producing your own is easy. For two weeks before building, record time per item, the error or correction rate, and how long a request waits before reaching the next person. Repeat the same measurements once the workflow runs, and add the share of items the model hands to a person as unknown.

How we approach it at Corrplus

In the first conversation we walk through a week of your team’s work and list the steps worth automating. In design we decide the tool for each step (rule, RPA or language model), the approval points and the data protection review, and put them in writing. We build in short cycles and hand over a version running on real data at the end of each one. See our workflow automation service, or start with the two-week AI discovery sprint. We work from Istanbul in English and Turkish.