The pilot worked. Can the review team handle ten times the work?

A pilot processes twenty cases a week. The agent is fast, the reviewers know every account, and exceptions get resolved in chat. The sponsor approves two hundred cases. Model capacity is available immediately; reviewer capacity is not.

Expansion should test the slowest required human or system step. A process can produce drafts faster while delivering accepted results more slowly, because the queue before approval grows. Estimate that queue before promising the business a new service level.

A practitioner discussion about approval bottlenecks describes the operational concern. The buyer decides whether to add capacity, cut unnecessary review, narrow the workflow, or expand gradually.

Count arrivals and service time#

Start with review requests per day and active reviewer time on each. Separate routine requests from exceptions, which take far more time and need a different person, so one average hides the bottleneck.

Suppose 200 weekly cases need four minutes of routine review each: thirteen hours and twenty minutes. If 20% also need fifteen minutes of specialist attention, add ten specialist hours, which the pilot hid because its builder handled them informally.

Then place the work on the calendar. Thirteen hours spread across a week differ from thirteen arriving Friday afternoon. Review capacity must exist when decisions are needed; a named backup who is also busy at month-end provides no resilience.

Distinguish review from permission#

Some review checks output quality and some approves authority to act. They often happen together, and removing one leaves the other: an accurate draft can still require permission to make an external commitment.

For quality review, find repeated mechanical checks a tested method or deterministic validation can perform. For authority, define a narrow class of actions the business will preauthorize, keep exceptions visible, and verify that the workflow recognizes them.

Do not solve overload by asking reviewers to click faster. If the proposal is hard to inspect, improve the evidence packet: changed fields, source references, unusual conditions, and intended destination.

Rehearse a peak week#

Use a synthetic or read-only queue shaped like the expected peak, with absent reviewers, a burst of difficult cases, and a slow source system. Measure the oldest pending item and the time to accepted completion, counting more than drafts.

Agree on fallback rules:

• Low-priority drafts wait.
• A customer deadline triggers the existing manual process.
• Stale proposals get a fresh source check before approval.

Each rule preserves the business commitment and never treats every queued item as equally urgent.

LangGraph's interrupt mechanism preserves work while waiting for input and supplies no staffing plan; a durable queue still needs someone who can decide before the result becomes useless.

Expand one constraint at a time#

If review capacity is the constraint, a smaller expansion with a dedicated reviewer can outperform a broad launch. If exceptions dominate, improve classification or narrow accepted inputs. If destination limits dominate, more reviewers will not fix the queue.

Track work removed as well as added: an agent can eliminate preparation time for one team while increasing review time for another. The sponsor should see both sides before claiming a net saving.

Use the rollout checklist for operational readiness and the approval instructions for the reviewer action. Enterprise covers cross-team adoption and its controls.

Approve the next volume step with an explicit service promise and an owner for the constrained step, then compare promised and observed completion times after expansion. The test is whether more business work finishes acceptably with sustainable human attention.