> Source: https://builder-docs.ema.ai/getting-started/effective-discovery/hire-to-retire > Title: Hire to Retire Discovery Guide # Hire to Retire Discovery Guide This guide provides a structured framework for conducting discovery for AI Employees that support the employee lifecycle -- from talent acquisition through onboarding, employee support, and offboarding. ## Product Suite Overview Ema's Employee Experience (EX) suite consists of specialized AI Employees that support different stages of the employee journey: AI Employee Purpose **Job Description Generator** Creates comprehensive, inclusive job descriptions using structured inputs. **Market Intelligence Generator** Benchmarks roles and salaries with real-time labor market data. **Resume Ranking Assistant** Extracts, scores, and ranks candidate resumes against job requirements. **Leadership Recruiter** Automates executive sourcing and engagement. **Onboarding Assistant** Generates offer letters and employment contracts. **Employee Assistant** Delivers real-time support and workflow automation for employees. Each AI Employee can be deployed independently or together, based on organizational needs and technical readiness. ## Discovery Preparation ### Required Knowledge - Familiarity with HR systems (ATS, HRIS, case management, payroll). - Understanding of HR lifecycle workflows and role responsibilities. - Awareness of data privacy, compliance, and security expectations. ### Stakeholders to Involve - HRIS and HR operations teams. - Talent acquisition leads and recruiters. - IT, Security, and Compliance. - Legal, Procurement, and Policy owners. - End users such as HR business partners and employee experience owners. ### Recommended Pre-Work - Gather HR process documentation and system maps. - Identify available sandbox or test environments. - Clarify scope of AI Employee(s) and expected outcomes. - Confirm access to sample data (e.g., resumes, job descriptions, support tickets). ## End-to-End Workflow Mapping ### Employee Lifecycle Discovery Map the complete employee lifecycle to ensure AI Employees align with real operational workflows. **What to capture:** - **End-to-end journey stages:** Document each phase from hiring through onboarding, employee movement, and offboarding. - **Cross-system data transitions:** Clarify how data flows between systems like ATS, HRIS, payroll, and benefits. - **Human approvals and checkpoints:** Identify where decisions are made by people (e.g., offer approvals, policy exceptions). These are areas where AI can assist, not automate. - **Manual bottlenecks:** Highlight repetitive or high-effort tasks that burden HR staff. - **Compliance and audit steps:** Understand where validations or approvals are required by legal, policy, or union agreements. **Discovery methods:** - Live walkthroughs with HR staff narrating recent cases. - Shadowing or screen recording reviews. - Stakeholder interviews across HR, IT, legal, and operations. - SOP reviews cross-checked against actual practices. ## Functional Discovery by AI Employee ### Job Description Creation **What to capture:** - Trigger events that initiate JD creation (requisition, attrition, expansion). - Existing templates and storage locations. - Approval and review flows. - Update frequency and ownership. - Compliance and DEI considerations (localization, EEOC). ### Resume Evaluation **What to capture:** - Resume formats and parsing reliability. - Scoring and ranking logic (skills match, experience fit). - Required extraction fields (certifications, education, years of experience). - Bias mitigation practices. - Explainability needs for regulated environments. ### Executive Hiring **What to capture:** - Leadership competency definitions. - Sourcing channels (LinkedIn, referrals, search firms). - Outreach personalization approaches. - Assessment methods and scorecards. - Market monitoring practices. ### Employee Support **What to capture:** - Most common employee requests and queries. - Current support channels (chat, email, portal). - Manual touchpoints where HR is overloaded. - Region- or policy-specific variations. - Multilingual needs and UX constraints. ## Success Metrics AI Employee Metric Description Job Description Generator Time to Create Average time to generate a new JD. Job Description Generator Accuracy Rate Percentage of JDs requiring minimal edits. Resume Ranking Assistant Time to Screen Time saved in initial resume filtering. Resume Ranking Assistant Quality Score Percentage match between ranked and hired candidates. Leadership Recruiter Time to Fill Days from search to shortlist. Leadership Recruiter Engagement Rate Percentage of executives who engage with outreach. Employee Assistant Query Volume Number of queries handled autonomously. Employee Assistant Accuracy Rate 1 minus percentage of responses rated negatively. ## Pre-Launch Evaluation Checklist - Golden dataset of sample queries or resumes prepared. - Internal quality thresholds defined (e.g., 85% accuracy). - SME validation workflows established. - Test environment coverage confirmed with mock data. - Baseline metrics captured for comparison. ## Stakeholder Roles Role Responsibility Executive Sponsor Strategic alignment, resource allocation. HRIS Lead Tool integration and technical ownership. Talent Lead Domain input for AI evaluation and QA. Knowledge or Policy Owner Document access, tagging, and updates. IT or Security Auth setup, access control, observability. End Users Real-world feedback and usability testing.