Moving approvals online: where AI belongs in a process
Most process automation failures mix judgement with execution. How to classify nodes: deterministic rules, AI agent steps, and where approval belongs.
The first attempt at process automation usually hands everything to AI: read the email, infer intent, decide whether to approve, send the notice. Three months later nobody can explain why a specific case was approved.
Classify every node first
- Deterministic steps — anything expressible as a rule: amount thresholds, field validation, approver lookup. Keep them as rules. They reproduce and they audit.
- Understanding steps — extracting structure from unstructured input: what is this email asking for, what does the screenshot show. Good fit for AI agent nodes.
- Decision steps — approve or reject, how much to pay. Keep them human. AI can supply evidence and a recommendation, not the verdict.
The visual designer in GeniusFlow is built around this split: agent nodes sit between ordinary nodes.
Put approval before irreversible actions
The practical rule: require a human immediately before anything irreversible — sending money, publishing externally, deleting data. Everything leading up to it can be automated.
Make the process observable
After launch, every step should record its input, output, duration and whether a person or an agent executed it. This is not for auditors; it is how you find the node to fix.
Start with one process
Do not migrate ten workflows at once. Choose one that is rules-heavy, high-frequency and low-cost when wrong. Run it for a month, close the edge cases, then copy the pattern.
GeniusFlow is in beta, aimed at teams validating a single process before scaling.
Details and access on the GeniusFlow product page.