Published on 19/09/2026
Does your process really need AI?
An inbox is overflowing, documents have inconsistent names and someone suggests, "Let's do it with AI." The impulse is understandable. But AI is not a mark of process quality. For many office tasks, fixed rules are easier to understand, test and control.
The useful question is not "Where can we use AI?" but "What kind of decision is this?" Three fictional tasks make the distinction concrete.
Task 1: distribute known files
A fictional business receives PDFs in a central inbox each day. Known senders include a job number in the subject. That number determines the destination folder and responsible employee.
Deterministic rules are enough: accept PDFs only, check the job number against the job list, apply a standard file name and place the file in the matching folder. If the number is missing or unknown, send the case to a review list.
AI adds little. It could even guess a plausible but incorrect number. A rule returns either "found" or "not found". With structured data, that limitation is often valuable.
Task 2: capture inconsistent documents
In the second fictional case, order confirmations arrive from several suppliers. Delivery date, reference and amount appear in different positions, so a fixed coordinate rule performs poorly.
AI can interpret text and context, then propose field values. The workflow must not turn that proposal into certainty. Each output needs a connection to the original, technical validation and a route for uncertainty. An amount can be checked for numeric format, a reference against known jobs and a date for plausibility.
A confidence score only describes how confident a model is in its output. It does not prove that the output is correct. A high-confidence answer can still be wrong. The business should therefore test its own sample documents and decide which fields may continue automatically, which require review and when the entire case must stop.
Task 3: decide a customer complaint
In the third fictional case, a customer says work was incomplete and requests a credit. AI can summarize the message, collect job details or prepare a draft response. Deciding whether the request is justified and which commercial response fits still requires context, responsibility and human judgment.
An automatically sent promise could affect money, the customer relationship and future work. The appropriate endpoint is a decision pack, not an automatic decision. A responsible person reads the original and case record, edits the draft and explicitly approves the action.
Decision table
| Task characteristic | Fixed rules | AI interpretation | Human judgment |
|---|---|---|---|
| Input | structured fields, known codes | variable text or document layouts | incomplete context, competing interests |
| Output | unambiguous calculation | suggestion or classification | accountable decision |
| Error route | reject invalid case | flag uncertainty and hand over | ask, weigh options, approve |
| Example | match a job number | suggest fields from a confirmation | resolve a complaint |
| Control | rule tests and log | samples, routing threshold, original comparison | named responsible person |
Many good solutions combine all three columns. Rules first check file type, sender and available master data. AI handles only the inconsistent content. A person approves consequential actions. This split limits errors and makes the workflow explainable to employees.
Structure before intelligence
Improve the input before adding a model. A required job number on a form is better than guessing it later. A selection list is more dependable than ten spellings of the same status. One shared inbox is easier to monitor than personal forwarding rules.
Then create a small test set containing normal, difficult and invalid cases. Remove or protect sensitive material appropriately. Define the expected outcome before running the test. Check not only successful matches, but also whether failures remain visible and the workflow stops safely.
Evaluate a bounded trial
Split the sample in advance into examples for configuration and separate examples for evaluation. If a team repeatedly adjusts rules or instructions against the same ten documents and then assesses only those documents, it mainly measures adaptation to known cases. Hold several documents back and use them only for the final check.
Keep a simple error log: expected value, proposed value, type of error and required action. Distinguish a visible stop from an unnoticed wrong transfer. A workflow that asks for help on two difficult cases may suit the business better than one that processes nine automatically and posts the tenth incorrectly with convincing confidence.
Measure review time too. If employees must compare every field with the original, extraction may not yet reduce work. Narrower document types, better input structure or fixed rules for selected fields may help more than lowering a confidence threshold.
Four questions before using AI
- Can clear rules determine the result?
- Is the input genuinely unstructured, or merely poorly organized?
- What happens after a wrong output, and can it be reversed?
- Who reviews uncertain or consequential cases?
If the first two questions point towards rules and structure, start there. AI earns its place when genuine interpretation is needed and a controlled failure route exists. To separate a workflow into rules, AI assistance and human approval, use the DevSeal process check to define a sensible first scope.