Recruiting · Production

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

Centralized candidate data ingestion

trigger

“Greenhouse became the one place where all candidate information lived. Every note, piece of feedback email and stage decision gets recorded right in Greenhouse.”

AI-powered candidate filtering

ai_action

“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”

Automated stage transitions and self-scheduling

integration

“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-a…”

Interviewer group coordination

human_review

“interviewer groups organized interviewers by team and skillset with auto-replacement, keeping schedules moving”

Real-time reporting dashboards

output

“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 ra…”

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

Source

https://www.greenhouse.com/customer-stories/kaizen-gaming-cuts-time-to-hire-by-33-percent

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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.

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.