Why teams feel slower after adoption

Teams often report this paradox: draft speed improves, but end-to-end completion does not.
The root cause is usually not model intelligence. It is weak handoff design between agent output and human approval.

If outputs cannot move cleanly into the next stage, humans become rewriters instead of reviewers.

Three recurring failure patterns

Unclear role boundary
If there is no fixed line between agent-generated scope and human decision scope, accountability and quality both drift.
No explicit definition of done
"Good enough" is an expensive standard. Without fixed format, evidence, and guardrails, the same edits repeat.
No re-request protocol
When correction requests vary by person, logs accumulate but learning does not.

Checklist: what to lock before rollout

Role split: document generation vs approval responsibilities
Quality criteria: fix output format, required evidence, and forbidden items
Approval gate: require human approval for external send and policy-sensitive outputs
Ops logging: record intent, correction reason, and final approval time
Fail-safe: define stop conditions for error states

Step 1: Label Failure Cases (Day 1-2)

Label 10 failed cases into 3-5 re-request categories.

Identify the most frequent failure patterns and assign clear labels to each. For example: "Insufficient Evidence", "Format Error", "Policy Violation".

Output: List of failure types (3–5) with 2–3 examples per type

Step 2: Apply Completion Templates (Day 3-4)

Apply a fixed done-template to two high-frequency tasks.

Lock down submission format and mandatory conditions to prevent patterns from Step 1. Agent outputs must meet these criteria before approval.

Output: Completion template (format, required items, prohibited conditions)

Step 3: Lock Review Items (Day 5-6)

Lock review items for delay-heavy approval stages.

Track actual delays and define clear review checklists for bottleneck stages.

Output: Stage-by-stage review checklist (2–3 items per stage)

Step 4: Compare Metrics (Day 7)

Compare re-request rate, approval lead time, and rollback count.

Collect 7 days of data and measure improvement in rejection rates, approval times, and resubmission frequency.

Evaluation Criteria: 20%+ reduction in re-request rate OR 30%+ reduction in approval time

This 4-step routine usually reduces quality variance without endlessly expanding prompts.

Editorial takeaway: change the KPI first

Many teams over-focus on draft generation speed. In production, time-to-final-approval is the KPI that actually reflects business value.
If a draft is faster by one minute but approval takes twenty minutes longer, total productivity declines.

Core execution summary

Item	Practical rule
Role split	Make generation vs approval ownership explicit
Done criteria	Fix format, evidence, and guardrails in templates
Revision loop	Standardize correction reasons by pattern
Ops metrics	Track approval time, re-request rate, rollback rate weekly
Expansion rule	Expand automation scope only after 2 weeks of sustained improvement

FAQ

Q1. Doesn't documentation slow teams down at first?

Slightly, but repeated work usually becomes faster as re-request loops shrink.

Q2. Can't we solve this by writing better prompts?

Prompt quality helps, but without role boundaries and done criteria, the same failures return.

Q3. What is the first metric to monitor?

Start with final approval completion time, not draft speed.

operating model Posts

Agent Handoff Checklist to Reduce Approval Delays