Yes AI. AI consulting for Australian businesses.
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Most Australian businesses run exit interviews, take notes, file them, and look at them once a year. The patterns that explain much of your attrition sit in those notes uncategorised. AI changes the math: every exit interview synthesised into themes, themes tracked over time, attrition drivers surfaced before they hit the next cohort.
Used by Australian businesses with 50 to 1,000 staff to convert exit interview data from filing-cabinet content into retention strategy.
Four classic failure modes in exit interview programs all have the same root cause: synthesis cost.
A human cannot read 200 exit interview transcripts and surface patterns. AI reads all 200 and clusters into themes with frequency and intensity.
AI surfaces hypothesised attrition drivers: "manager X, 4 of 6 departures cite micromanagement", "engineering compensation, 70 percent of senior departures cite below-market pay". Each with quantitative weight.
Departures cluster: by team, by manager, by tenure band, by role family, by department. AI surfaces cohort-specific patterns that individual interviews hide.
AI drafts the retention strategy implied by the patterns: which programs to prioritise, which managers need coaching, which compensation bands need review.
Five stages, from individual exit to retention strategy.
Ingest from existing tools (BambooHR, Workday, SurveyMonkey exits) or in-house templates.
AI clusters reasons-for-leaving and free-text into themes with frequency, intensity, cohort distribution.
Drafts attrition driver hypotheses: manager-level, team-level, comp-level, role-level. Evidence per hypothesis.
Surfaces patterns by team, manager, tenure, role family. Highlights cohort-specific issues.
Drafts the retention strategy: priorities, owners, interventions, expected impact. Board-ready narrative.
Pattern analysis must not enable re-identification of specific exit feedback. We design data architecture to surface patterns above anonymity threshold (typically 3 to 5 minimum cohort size).
AI surfaces manager-level patterns. Use the signal for coaching and development, not punishment. Punishment culture destroys exit interview honesty and the data dries up.
Exit interview themes may not perfectly reflect actual reasons for leaving (exit politeness, recency bias). Triangulate with engagement survey data, manager check-in data, departure timing patterns.
Exit interview data is personal information about both the exiting employee and named managers. APP 11 (security), APP 6 (use), APP 11.2 (retention) apply. We design APP-compliant data architecture.
Hypotheses from exit data should be validated by current employee voice (engagement survey, stay interview, focus group) before triggering major intervention.
Intervention implemented today should be measured against next-cohort attrition. AI tracks theme frequency before / after intervention. Closing the loop validates investment.
Ingest from existing exit interview tools or templates. Structure free-text and quantitative data for analysis.
Build the AI pipeline: theme clustering, attrition driver hypothesis, cohort segmentation, retention strategy drafting.
60 minute training on reading the synthesis and using it for retention strategy and board reporting.
Quarterly review with CHRO. New patterns, intervention impact, board reporting narrative.
Most AU clients have pattern analysis on existing exit interview data inside 14 to 21 days.
Audit existing exit interview data. Build ingestion pipeline. Lock anonymity threshold and reporting structure.
Build the AI pipeline: theme clustering, driver synthesis, cohort segmentation, retention drafting.
60 minute training on reading synthesis. Stage first board narrative.
Each new exit interview adds to the pattern. Patterns tracked quarterly. Interventions targeted.
Pattern review, intervention impact, board narrative refresh.
Validate exit patterns with current employee voice.
Manager development data feeds retention.
Full HR Claude playbook.
AI handles unstructured free-text. We work from whatever you have: typed notes, recorded transcripts, survey responses. Some structuring of new exits improves pattern detection over time.
No, designed not to. Themes are surfaced at cohort level above anonymity threshold (typically 3 to 5 minimum). Individual comments paraphrased and themed, not directly quoted.
Integrates with BambooHR, Workday, ELMO, SuccessFactors. Exit interview data ingested; pattern analysis returned to HRIS as analytics output.
Yes, above the anonymity threshold (5 departures per manager typically). Manager coaching or development decisions triggered by pattern, not by individual exit feedback.
Designed APP-compliant. Personal information secured under APP 11. Use limited to retention strategy (consent-aligned under APP 6). Retention period defined under APP 11.2.
AI drafts the board attrition narrative: top drivers, cohort-specific issues, intervention progress, attrition trend. Board paper ready in hours.
Productized: $8,000 setup (data audit, pipeline, training). $500 to $1,500 per quarter for managed analysis and quarterly review. For a 200-person AU business, total annual cost is $10K to $14K.
Pattern detection means intervention is targeted: manager coaching for the manager whose direct reports keep leaving, compensation review for the function with comp-driven exits, career path program for the tenure-band with growth-driven exits. Targeted interventions cost less and work better than spray-and-pray programs.
30 minute demo on a sample exit data set. We show pattern detection, attrition driver synthesis, and retention strategy drafting.
All discussions held in confidence. Australian-based consultants.