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.
Performance Review · People Development Cycle
A design case study from the AI HR capability: employees update evidence, managers review, HR coordinates calibration and employees respond to results.
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.
Sample UIs use synthetic data. Existing DocAgent screenshots have record/partner/figure data redacted. No real customer or employee results are used for illustration.
Goals
Publish criteria, weights and permissions before the cycle.
Evidence
Employees update work and self-review.
Review
Managers check against the rubric and write evidence-based comments.
Calibration
HR checks consistency, coordinates and publishes within rights.
Development
Employees respond; actions and coaching milestones are agreed.
See where the work gets done.
People Development workspace
Prototype dashboard with simulated records; states and process follow the solution design.
Open the sample UI ↗
People Development assistant
How do I draft a review from evidence? Sourced simulated scenario; no LLM call.
Open the sample UI ↗Every role has a clear workflow.
The apps focus on frequent tasks. In production they share the APIs, file codes, permission policy and history of the business platform.
GOALS & EVIDENCEEmployee
- View the goals and criteria for the cycle.
- Submit evidence and the self-review.
- Respond to results once published.
REVIEW & COACHINGLine manager
- Read employee evidence within the managed scope.
- Check against the rubric, write comments and development suggestions.
- Send the file to calibration.
CYCLE QUALITYHR / Calibration committee
- Check process completeness and the basis for comments.
- Coordinate calibration and record reasons for any change.
- Publish within scope and receive responses.
Module mapping & permission limits
| Role | Module | Scope |
|---|---|---|
| Employee | Goals → Evidence → Self-review → Response | Own file only; cannot view colleagues’ reviews. |
| Line manager | Evidence → Review → Development plan | Assigned employees only; cannot publish results that skipped required steps. |
| HR / Calibration committee | Progress → Calibration → Publish → Response | Per review-cycle policy; admin rights are not used to disclose data beyond scope. |
From input to an evidence-based decision.
The first role completes the checklist and submits evidence; the next role records an evidence-based result; the approver confirms or returns with a reason. The sample session logs every step.
After HR publishes the sample result, the employee can respond into the same history. There is no automatic scoring and no pay/discipline decision.
No automatic scoring, no pay/discipline decisions, no inference of emotion or personality.
Sample state is stored in the same browser, separated per solution. There is no authentication, multi-user backend, cross-device sync, push or LLM connection. Those must be built and accepted before real operation.
Sources to compare, not automatic conclusions.
EVIDENCE-DEMO v1
Simulated work-completion documents with no real employee names or results.
RUBRIC-DEMO v1
Reviews based on goals, output quality and collaboration; the manager is accountable for comments.
POLICY-DEMO-HR
Employees may respond. No biometrics, inferred emotion or AI decides the final score.
The draft should list the available evidence, what is missing and questions for the conversation. This sample lacks the data to conclude on performance. The manager checks and decides the content; the AI does not score or make HR decisions.
DEMO sources are synthetic data for simulation. Real business documents must be collected with usage rights, versioned and quality-checked.
Pilot & acceptance roadmap
- Choose one position group; agree the rubric and access policy.
- Build a cleared evidence set and the review forms.
- Run a trial cycle with three roles, calibration and responses.
- Accept access rights, review basis and development actions.
Conditions to verify
- Submission only when the self-review has evidence and a complete checklist.
- Reviews are evidence-based before HR publishes.
- Employees can respond within their own file.
- No automated decisions on scores, pay or discipline.
Agree scope, baseline, sample size, confirming person and operating responsibility before the pilot. Demos do not replace acceptance results or performance commitments.
Reference direction: CTC Smart Tech AI HR & Performance Analytics direction. Details are compiled from existing documents and source code; new UIs are clearly marked as proposed designs.
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.