AI Approval Workflows: Smarter Routing, Summarising & Escalation

What are AI approval workflows?

Every business has approval processes: purchase orders, expense claims, leave requests, change requests, new vendor onboarding, contract sign-offs. Most of them follow a predictable pattern: someone submits a request, someone else reviews and approves it, and the workflow moves forward.

The problem isn't the approval itself. It's everything around it: requests sitting in inboxes, approvers lacking context, documents attached without summaries, requests routed to the wrong person, exceptions buried in routine items.

AI approval workflows add intelligence to this process. AI reads the request, summarises the supporting documents, routes it to the correct approver based on the content (not just a static rule), and flags anything unusual, so the human approver can make a faster, better-informed decision.

Why approvals break down

Approval bottlenecks are one of the most common complaints in mid-size businesses. The reasons are surprisingly consistent:

• Wrong routing. Requests go to someone who has to forward them to someone else. Each redirect adds a day or more.
• Missing context. The approver receives a request with attachments but no summary. They have to open multiple documents, understand what they're looking at, and figure out what they're being asked to approve. Most approvers have dozens of these in their inbox.
• Inconsistent classification. A $5,000 expense claim might need one level of approval, while a $50,000 one needs three. If the submitter miscategorises the amount or type, the routing is wrong from the start.
• No visibility. Nobody knows where a request is stuck. The submitter doesn't know if it's been seen. The approver doesn't know it's urgent. Management doesn't know there's a bottleneck.
• Exceptions hidden in volume. The one unusual request that actually needs scrutiny is buried in 50 routine ones. The approver clicks "approve" on all of them because they don't have time to review each one properly.

How AI improves the process

AI adds value at three points in the approval workflow:

1. Intelligent routing

AI reads the request content (not just the category field) and routes it to the correct approver based on what's actually in it. A purchase order for IT equipment goes to the IT budget holder. A purchase order for marketing materials goes to the marketing lead. Even if both are labelled "purchase order."

2. Document summarisation

When a request includes attachments (quotes, contracts, specifications, invoices), AI reads them and produces a brief summary: what's being requested, from whom, for how much, and what's notable. The approver gets context in 30 seconds instead of 15 minutes.

3. Exception flagging

AI identifies requests that don't match typical patterns: unusually large amounts, first-time vendors, requests that conflict with policy, items that have been rejected before. These are flagged for closer review, while routine approvals can be fast-tracked.

Practical use cases

Purchase order approvals

AI reads the PO, identifies the category and amount, routes to the appropriate budget holder, summarises the attached quote or proposal, and flags anything unusual (e.g., a vendor not on the preferred list, an amount above budget threshold).

Expense claims

AI categorises expenses, matches receipts to claimed amounts, routes to the correct approver based on team and amount, and flags policy exceptions (e.g., entertainment expenses over the limit, claims without receipts).

Leave requests

AI checks team availability, identifies conflicts (e.g., two team members requesting the same dates), routes to the relevant manager, and provides context on coverage. Simple for the 90% of requests with no conflicts.

Contract approvals

AI summarises the key terms of a contract (value, duration, obligations, termination clauses), identifies which approval level is required, and routes accordingly. Legal review is triggered automatically for contracts above a threshold or containing non-standard clauses.

Change requests (IT/engineering)

AI reads the change request description, assesses risk level based on the systems affected and the type of change, and routes to the appropriate change advisory board or individual approver.

Risks and limitations

• Don't remove humans from the loop. AI should inform and accelerate approval decisions, not make them. Accountability for approvals must remain with people.
• Routing accuracy depends on data. AI needs clear, consistent data to route correctly. If your request forms are free-text fields with no structure, routing accuracy will suffer. Add structure where it helps.
• Edge cases will always exist. AI handles the majority well, but unusual requests (first-of-type, cross-departmental, politically sensitive) still need human judgement. Build escalation paths, not exceptions.
• Over-automation risk. If approvals become too easy, people rubber-stamp everything. The goal is faster informed decisions, not faster uninformed ones.

Getting started

1. Pick one high-volume approval type. Purchase orders, expense claims, or leave requests. Something with clear rules and consistent structure.
2. Map the current workflow. Who submits, who approves, what are the routing rules, where does it get stuck?
3. Identify the AI insertion points. Where would summarisation, routing, or flagging save the most time?
4. Build and pilot. Deploy AI-assisted routing and summarisation alongside the existing process. Let approvers compare the AI-routed version with what they would have done manually.
5. Expand. Add more approval types, integrate with your ERP or workflow system, and automate the handoffs.

Frequently asked questions

Can AI auto-approve routine requests?

Technically yes, but we recommend against it for anything with financial or legal implications. AI can fast-track routing and summarisation so the approver spends seconds instead of minutes, but a human should still click "approve."

What systems does this integrate with?

Common integrations include ERP systems (SAP, NetSuite, MYOB), project management tools (Jira, Asana), HR platforms, and email/Slack for notifications. Most systems with APIs can be connected.

How accurate is the routing?

With well-structured request forms and clear organisational data, routing accuracy is typically 90–95%. The remaining 5–10% are edge cases that get escalated for manual routing, which is still better than the current state.

Does it work for small teams?

The value scales with volume. If you process 10 approvals a week, the setup cost isn't justified. If you process 100 or more, the time savings compound quickly.

Kasun Wijayamanna Founder & Lead Developer

Postgraduate Researcher (AI & RAG), Curtin University - Western Australia

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