Kaizen Gaming cuts time-to-hire by 33% with Greenhouse centralized recruiting and AI-powered screening
The problem
Kaizen Gaming's talent acquisition team relied on disconnected tools and spreadsheets across 20 international markets, with no visibility into pipeline health or recruiter performance, making it impossible to identify bottlenecks, enforce consistent processes, or ensure GDPR compliance.
Workflow diagram · grounded in source
1
Centralized candidate data ingestion
Trigger
Greenhouse became the single place where all candidate information lived, with every note, piece of feedback, and stage decision recorded there.
▾ source quote
“Greenhouse became the one place where all candidate information lived. Every note, piece of feedback email and stage decision gets recorded right in Greenhouse.”
2
AI-powered candidate filtering
Ai action
Talent Filtering handles the initial heavy lifting of sorting through applications, instantly surfacing the best-fit candidates.
▾ source quote
“Talent Filtering handled the initial heavy lifting of sorting through applications, instantly surfacing the best-fit candidates. This freed up hours of manual CV screening so recruiters could focus on what actually requires human judgment”
3
Automated stage transitions and self-scheduling
Integration
Stage transitions and routine tasks happen automatically, and self-scheduling lets candidates pick their own interview times, eliminating email back-and-forth.
▾ source quote
“Stage transitions and routine tasks now happen automatically, cutting out manual handoffs. This means that interview scheduling also became a breeze. Self-scheduling let candidates pick their own interview times, eliminating email back-and-forth.”
4
Interviewer group coordination
Human review
Interviewer groups organize interviewers by team and skillset with auto-replacement, keeping schedules moving.
▾ source quote
“interviewer groups organized interviewers by team and skillset with auto-replacement, keeping schedules moving”
5
Real-time reporting dashboards
Output
The Greenhouse BI Connector pulls all recruiting and HR data into Power BI, where the team built custom dashboards tracking pipeline health, recruiter performance, and stage conversion rates.
▾ source quote
“Comprehensive reporting tools and the Greenhouse BI Connector let the team pull all recruiting and HR data straight into Power BI, where they built custom dashboards tracking pipeline health, recruiter performance and stage conversion rates.”
Reported outcome
Time-to-hire dropped 33% (from 54 to 36 days), average cycle time per stage improved 31% (from 6.5 to 4.5 days), and time-to-start fell 11% (from 75 to 67 days). The team also reduced compliance risk and gave hiring managers real-time visibility into their pipelines.
Reported metrics
Time-to-hire reduction33%
Time-to-hire (days)from 54 days to 36 days
Average cycle time per stage improvement31%
Average cycle time per stage (days)from 6.5 days to 4.5 days
Show all 7 reported metrics
time-to-hire reduction33%
time-to-hire (days)from 54 days to 36 days
average cycle time per stage improvement31%
average cycle time per stage (days)from 6.5 days to 4.5 days
time-to-start reduction11%
time-to-start (days)from 75 days to 67 days
manual CV screening hoursfreed up hours of manual CV screening
Reported stack
GreenhouseTalent FilteringGreenhouse BI ConnectorPower BI
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Time-to-hire dropped 33% (from 54 to 36 days), average cycle time per stage improved 31% (from 6.5 to 4.5 days), and time-to-start fell 11% (from 75 to 67 days).
What tools did this team use?
Greenhouse, Talent Filtering, Greenhouse BI Connector, Power BI.
What results were reported?
Time-to-hire reduction: 33%; Time-to-hire (days): from 54 days to 36 days; Average cycle time per stage improvement: 31%; Average cycle time per stage (days): from 6.5 days to 4.5 days (source-reported, not independently verified).
How is this recruiting AI workflow structured?
Centralized candidate data ingestion → AI-powered candidate filtering → Automated stage transitions and self-scheduling → Interviewer group coordination → Real-time reporting dashboards.
This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.