hr_ops · workflow
Miro's peer feedback participation jumps from 50% to 93% in eight weeks using Zapier automation and AI-generated summaries
Miro's engineering peer feedback cycle had only 50% participation (roughly 400 reviews) because the formal HR system was painful: requesters received generic broadcast Slack messages pointing to unfamiliar HR software, reviewers could not see how many reviews they owed, and managers had to manually copy each review into a Google Doc one at a time.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Personal DM nomination
A Zap reads from a Zapier Table, finds each engineer by their Miro email in Slack, and sends a personal DM opening a Slack workflow.
Tools used
ZapierZapier TableZapier PathsGoogle Sheets · partnerSlack · partnerWorkday · partnerGemini Gem
Outcome
Participation rose from 50% to 93% in eight weeks, and reviews grew from roughly 400 to over 1,600. Each daily nudge lifted participation by roughly 10%.
Results
Time savedeight weeks
Volume50% to 93%
Grounding & classification
Source type: vendor customer story
32 fields verified against source quotes.
ai agentsummarizationemailform submissionfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarecycle time reductionemployee productivitythroughput increasevendor customer storyhr onboardinghr opsai draft human approvalescalation workflow