Automated Flight Risk & Retention Scoring in Professional Services

Revenue Institute's AI model ingests utilization, billing, project staffing, and HR data across Workday PSA, Maconomy, Deltek, and Microsoft Project to identify consultants at risk of departure before they resign. The system learns from your firm's historical turnover patterns - which utilization profiles, promotion velocities, and client assignment patterns correlate with departures - and scores each consultant weekly against peer benchmarks. HR receives explainable alerts ("utilization down 20% YoY," "no new client engagements in 90 days") that pinpoint intervention points, enabling proactive reassignment, promotion, or compensation review before resignation risk peaks.

Yes, within the limits we're honest about. We apply reasonable administrative, technical, and physical safeguards to protect the data this system touches, and it is never used to train external models or shared across clients. No vendor can honestly promise absolute security, so don't take our word for it - ask to see our data-processing terms and put them in the contract before you sign.

Plan for a working system inside the first 100 days. Phase 1 (weeks 1-3) maps your Workday PSA, Maconomy/Deltek, and HR system integrations and validates data quality. Phase 2 (weeks 4-8) trains the model on your 24-month historical data and calibrates risk thresholds with your HR and finance leadership. Phase 3 (weeks 9-14) deploys the dashboard, trains HR and managing directors on interpretation, and runs parallel monitoring. A rollout like this is scoped to show measurable results - first retention saves, utilization improvements - within 60 days of go-live.

Three, in order of money. First, early warning: risk flags surface while a reassignment or compensation review still changes the outcome, instead of at the resignation conversation. Second, cheaper retention: an internal intervention costs a fraction of the external recruiting spend it replaces, and the backfill hire never happens. Third, visibility into causes: over time the data shows which practice areas, assignment patterns, and promotion bottlenecks push good consultants out - so the firm fixes the driver instead of paying for the symptom every year.

By day 30, the system is connected to your core platforms and shadowing real workflows so your team can validate accuracy against existing decisions. By day 60, it's running in production for a defined slice of work with humans reviewing outputs and a measurable baseline against pre-deployment metrics. By day 90, you have production-grade adoption: your team is operating from the system's outputs, you have a documented accuracy and exception-rate baseline, and you've decided which next slice to expand into. A rollout like this is scoped to show meaningful operational impact between day 60 and day 90, with the ROI case building through months 6-12 as the model learns your specific patterns.

That is your call, and we recommend making it deliberately rather than by default. The system reads operational data your platforms already record - utilization rates, billing realization, project staffing patterns - not private communications, and you can exclude any field from the model. Every intervention still requires human approval, so nothing reaches a consultant except a partner or HR deciding to act. Most firms position it internally the way it actually works: a tool that helps leadership notice when a good consultant is being under-utilized or overlooked for promotion before they start interviewing elsewhere.

No. Your current team stays - this is about the roles you have not posted yet. The system does the watching: it reads the Workday PSA, Maconomy or Deltek, and HR feeds weekly, scores every consultant, and drafts intervention options. Your HR team and managing directors keep every judgment call - who gets a conversation, what gets offered, and when. What changes is that HR stops finding out a consultant was unhappy from the resignation letter.