I wanted to share our successful implementation of automated learning and development course approval workflows that dramatically reduced our course deployment time. Prior to automation, getting a new course approved and published took 2-3 weeks due to sequential approval bottlenecks and manual coordination.
We implemented conditional routing based on course attributes - compliance courses go through legal and compliance review, technical courses route to subject matter experts, leadership development requires executive sponsor approval. Here’s a simplified version of our routing logic:
<RoutingRules>
<Rule type="compliance" approvers="legal,compliance_mgr"/>
<Rule type="technical" approvers="sme_pool"/>
<Rule type="leadership" approvers="exec_sponsor"/>
</RoutingRules>
Parallel approval processing was key - instead of sequential routing, multiple approvers receive requests simultaneously. Escalation rules for stuck approvals automatically remind approvers after 48 hours and escalate to their managers after 5 days. This eliminated the manual follow-up that consumed significant time.
The results: average approval time dropped from 12 days to 4.5 days, a 60% reduction. Course deployment cycles are now predictable and efficient.
We’re planning a similar automation project. One concern is ensuring that the automated workflow doesn’t sacrifice approval quality for speed. How do you ensure that reviewers are giving courses appropriate attention rather than just clicking approve to clear their queue? Are there any quality metrics you track beyond approval time?
The escalation rules are critical for maintaining momentum. We had a similar manual approval process where courses would sit in someone’s queue for weeks because they were busy or on vacation. Automated escalation with delegation support ensures that approvals don’t get stuck due to individual availability. Did you implement automatic delegation for approvers who are out of office, or does escalation go to their manager?
Good question. We implemented an approval policy where all parallel approvers must approve for the course to proceed. If any reviewer rejects, the entire approval fails and the course goes back to the author with consolidated feedback from all reviewers. This ensures that all perspectives are addressed before publication. We also added an optional ‘approve with comments’ option that allows reviewers to approve while requesting minor changes that don’t block publication.
The conditional routing based on course attributes is a best practice approach. It ensures that the right expertise reviews each course type without overburdening all approvers with every course. How granular did you make your course categorization? We’re considering a similar implementation and trying to determine the right balance between routing precision and category complexity.
The 60% reduction in deployment time is impressive. Beyond the time savings, have you seen other benefits from the automated workflow? I’m thinking about things like improved consistency in approval decisions, better audit trails, or reduced administrative overhead for the learning team.
Great use case. The parallel approval approach is smart - sequential routing is often the biggest bottleneck in approval workflows. Did you encounter any challenges with conflicting approvals when multiple reviewers are evaluating simultaneously? How do you handle situations where one approver rejects while others approve?