AI for approval workflows. Software that moves a pending approval forward on its own. It reads the request, checks it against your policy, routes it to the right approver in Slack, and records the decision. A human still signs off where policy says they must.
Most approval chains do not stall on the hard calls. They stall in someone's dms. A purchase order waits three days because the approver was traveling and nobody re-pinged. The decision took two minutes. The queue took a week.
#Why do approvals stall when the decision itself is quick?
Because the work is not the decision. It is the chasing. Someone has to find the request, pull the vendor's w-9, check it against the spend policy, and figure out who signs off at that amount. For a 60-person team that is dozens of small lookups a day. The approver never sees a clean request, so it sits.
What is AI for approval workflows?
It is software that moves a pending approval forward on its own. It reads the request, checks it against your policy, and routes it to the right approver in Slack with the context they need. The human still signs off where policy requires. The approver gets a clean request instead of a chase.
#What gets cleared faster: pos, vendor onboarding, access, and change requests
These four share a shape. A request comes in, it needs a check against a rule, then a named person says yes. That is where an ops bot earns its place.
- Purchase orders: match the amount to the approval threshold, attach the budget line, route to the right signer.
- Vendor onboarding: pull the w-9 and insurance certificate, flag anything missing, hold the request until it is complete.
- Access requests: check the role against the access matrix, route to the system owner, log the grant.
- Change requests: attach the affected tickets, route to the reviewer, record the sign-off for the audit trail.
We are
Stennir builds a Slack ops bot that approves, looks up, and routes, so the request lands ready to sign.
We aren't
It is not another summary bot that posts a recap and still leaves the chasing to you.
#Will it approve things on its own?
No, and that is the point. This maps to the internal ops bots on /ai-for/operations: bots that approve, look up, and route, not ones that auto-approve. The pattern is draft-and-route. The bot does the lookups and the routing, then a human signs off wherever your policy needs a name on the decision. You keep control of the calls that matter.
This shape has a worked example in the journal: a worked example of this pattern, an extraction plus rule-based validation plus human-approval pipeline for a generic logistics team. It is illustrative, with assumed numbers, not a client result. The approvers do not do less deciding. They stop doing the digging.
The bot does the lookups and the routing. The approver does the deciding. That split is the whole win.
#How do you build this without ripping out quickbooks and Salesforce?
You connect to what already runs your ops instead of replacing it. We deliver this through AI implementation, and the codebase is owned by you from day one. The bot connects to quickbooks or netsuite, Salesforce or HubSpot, your ticketing tool, and Slack. Nothing gets torn out.
Not sure which approvals to start with? That is a scoping question, and it is where our AI consulting work begins. We map where the queue actually stalls before anyone writes code, so the first thing we build is the one that clears the most days.
If your approval chains are dying in dms for days, that is a fixable problem, and it is a short conversation to scope. book a 30-min discovery call and bring your three slowest approval types. We will tell you which one clears first.
