Problem & audience
HR Directors, HRBPs, line managers and employees taking part in a review cycle.
- Unaligned goals and scattered evidence force manual synthesis at period end.
- Managers apply different standards and are swayed by the most recent events.
- Reviews stop at a score, with no coaching plan or follow-up after the cycle.
Solution modules
KPI / OKR & evidence
Sets goals, weights, scales and review periods; employees update results with evidence sources.
Check-ins & feedback
Self-review, manager conversations and 360 feedback within the allowed scope; progress logged across the period.
AI Review Copilot
Synthesises evidence into a draft review, citing sources and missing data; managers check and edit.
Calibration & development
HR coordinates calibration against a common standard; approves results, feedback and development-plan follow-up.
Process & data
Data to prepare
- Org structure, roles and reporting lines from the HRIS.
- Competency framework per position, goals, rating scale and review policy.
- Work evidence cleared for use; retention policy and feedback visibility rights.
Deliverables
- Review-cycle process, KPI/OKR forms and rubrics per position group.
- Permission matrix for employee, manager, HR and calibration committee.
- Review workflow, sourced drafts, calibration minutes and development-plan template.
- Case study with screenshots, a workspace and sample apps for three roles; module/permission matrix and handover flow.
Delivery conditions
The customer names a process owner, provides data cleared for use and a systems contact. CTC Smart Tech and the business team agree scope, test set, change handling and operating responsibility.
Infrastructure, equipment, licences, third-party integration and AI running costs are determined separately by discovery. This document is not a quotation or SLA commitment.
Pilot & acceptance criteria
- Choose one position group; agree the rubric, access rights and goals.
- Standardise sample evidence, forms and the review workflow.
- Run one trial cycle on permitted data and collect feedback from every role.
- Check consistency and access rights; finalise the adoption plan.
| Metric | How it is measured |
|---|---|
| Cycle progress | On-time completion rate for each step of the process. |
| Evidence quality | Share of reviews backed by relevant, verifiable evidence. |
| Rating consistency | Variance between reviewers on the same sample cases. |
| Post-review development | Share of coaching actions completed by agreed milestones. |
Fix the pass threshold, sample size and measurement period before the pilot. These metrics are a proposed evaluation frame, not actual results or a performance commitment.
Governance & control
- No faces, voices or EEG are used to infer emotion, personality or to score employee performance.
- AI never decides final scores, pay rises, discipline or termination.
- Employees can respond or request a re-check; every score change has an owner and a reason.
- 360 feedback is disclosed only per policy; groups too small to stay anonymous are avoided.
Apply least privilege, data separation, action logs, retention/deletion rules and recovery. Test wrong-permission access, missing data and service interruption before handover.
Explore the solution in a working prototype
A design case study from the AI HR capability: employees update evidence, managers review, HR coordinates calibration and employees respond to results.
Frequently asked questions
Does the AI score employees?
The AI helps gather evidence and draft comments. Scores and decisions belong to authorised people.
Can OKRs and competencies be combined?
Yes — as two separate parts with weights and rubrics published before the cycle; weights are never changed retroactively.
Reference: CTC Smart Tech AI HR & Performance Analytics direction. An AI HR landing and Performance Analytics direction exist; the review system, assistant and apps in this kit are proposed prototypes using no real HR data. The process, pilot and acceptance sections in this kit are proposed designs to be confirmed per project.