AI-assisted process automation is most useful when it helps a person complete a specific task: read a document, find missing evidence, summarise a case or prepare a response. It should not hide the source of a recommendation or take an unapproved decision on the business's behalf.
Start with the work, not the model
Pick one repeated step with a clear input, output and reviewer. For example, a supplier invoice arrives with a purchase order reference; the task is to suggest extracted fields for a clerk to confirm. This is different from asking AI to approve the invoice or change the ledger. Map the existing system of record and the person accountable for the final decision first.
Where AI assistance can add value
- Read: propose fields from a receipt or supplier document, showing the original image or text beside each value.
- Check: flag missing evidence or a mismatch for review rather than silently rejecting the case.
- Summarise: prepare a short handover of a long case history with links to the source events.
- Draft: suggest a customer or supplier response for an authorised person to edit and send.
- Prioritise: highlight an at-risk commitment using defined dates and rules, with the reason visible.
These are candidate use cases, not a statement that every feature is available in every Intelliflow deployment.
Separate rules, OCR and generative AI
A deterministic rule is suitable for a known condition such as “follow up two days before the due date.” OCR extracts text from a document but may misread it. Generative AI can summarise or draft, but may omit context or produce a plausible error. Use the least complex method that reliably solves the task, and test its failure modes before connecting it to a live decision.
Give the reviewer enough evidence
A useful suggestion shows what source was used, when it was processed, what was inferred and what the system could not establish. Let the reviewer correct or reject it. Keep the human decision and resulting action in the case history. For financial, legal, safety or customer-impacting decisions, require the appropriate authorised review rather than treating an AI answer as an approval.
Protect data and access
Before a pilot, identify what personal, commercial or confidential information the task uses; who may see it; where it is processed; and what is retained. Use realistic test material only under approved data controls. Do not put unrestricted customer records into a model merely because a prompt gives a useful answer. The deployment's data handling and permissions must be verified, not assumed from this guide.
Test the adverse cases
- Choose a small sample that includes clean documents and difficult examples: low-quality scans, missing pages, duplicate records and conflicting values.
- Record the expected answer and the evidence that supports it.
- Compare the suggestion with a human-reviewed result; count material errors, omissions and unnecessary escalations.
- Test that a reviewer can correct the output and that the system does not proceed after a rejected suggestion.
- Repeat after changing a prompt, model, document format or connected workflow.
Measure the outcome, not the number of prompts
Track handling time, correction rate, unresolved exceptions and whether the next owner receives better context. A faster draft is not a win if it creates more customer corrections or hidden rework. Keep a baseline without AI and review the results with the people who perform the task.
Illustrative example: supplier invoice intake
A supplier sends a scanned invoice. An assistant proposes the supplier, invoice number, date and amount, highlighting the source text. The clerk corrects a misread digit and confirms the purchase-order reference. A mismatch is routed to the purchasing owner; the invoice is not approved or posted by the AI. This is an illustrative design, not proof of a production feature or customer result.
How Intelliflow fits
Intelliflow can be considered as the process layer where a candidate AI-assisted step has a named owner, visible evidence and a controlled handoff. Confirm the exact AI, OCR, integration and review capabilities in the target environment before publishing product-specific promises or enabling a live path. To assess one real process, book a working demo.
Related reading and source
- Rules for repeatable follow-up
- NIST AI Risk Management Framework — voluntary guidance on managing AI risks and trustworthiness.
Intelliflow Editorial Team. Examples are illustrative; capability and data-handling claims require deployment-specific verification.