Cambridge
Academy
In London

Introduction

AI is transforming Talent Acquisition by improving sourcing reach, accelerating screening, and enabling smarter shortlisting—while raising important questions about fairness, privacy, transparency, and governance. This practical program equips TA leaders and recruiters with AI-enabled methods to strengthen sourcing intelligence, improve candidate matching, and streamline selection workflows with robust controls and human oversight.

Course Objectives

By the end of this course, participants will be able to:

·        Identify high-value AI use cases across sourcing, screening, and shortlisting workflows

·        Use AI to improve job requirements clarity, skills mapping, and candidate matching

·        Design AI-assisted screening processes with structured criteria and human review gates

·        Enhance sourcing intelligence using market insights, talent pool analysis, and outreach optimization

·        Apply responsible AI practices: bias mitigation, privacy, transparency, and documentation

·        Build a 90-day pilot plan and 12-month roadmap for safe AI adoption in TA

Target Audience

This course is designed for:

·        TA leads, recruitment managers, and senior recruiters

·        Sourcers and talent researchers using advanced sourcing methods

·        Recruitment operations and TA analytics professionals

·        HR business partners involved in hiring governance and workforce planning

·        Compliance, legal, and data privacy professionals supporting TA tool adoption

Course Outlines

Day 1: AI Foundations for TA & Use-Case Prioritization

·        Where AI fits in TA: job design, sourcing, screening, shortlisting, and scheduling

·        AI capabilities and limits: errors, hallucinations, explainability, and human-in-the-loop

·        Data readiness: ATS data quality, skill taxonomies, and consistent job architecture

·        Use-case value sizing: speed, quality-of-hire, candidate experience, and cost impacts

·        Activity: Build an AI-in-TA use-case backlog + value/feasibility prioritization matrix

Day 2: Sourcing Intelligence & Talent Market Insights with AI

·        Defining search strategy: skills, titles, industries, and adjacent talent mapping

·        AI-assisted persona building: ideal candidate profiles and must-have vs. nice-to-have skills

·        Talent market intelligence: supply signals, competitor mapping, and location strategy concepts

·        Outreach optimization: tailoring messages, sequencing, and response-rate improvement

·        Workshop: Create a sourcing strategy pack (ICP + Boolean search plan + outreach templates)

Day 3: AI-Assisted Screening & Structured Shortlisting

·        Structured screening criteria: competencies, skills evidence, and minimum requirements

·        AI screening workflows: resume parsing, skills extraction, ranking, and confidence thresholds

·        Human review gates: sampling, override rules, and escalation for sensitive roles

·        Designing shortlists: balancing speed, quality, diversity, and job fit

·        Practical activity: Screening simulation (AI-ranked candidates + human validation + shortlist rationale)

Day 4: Selection Quality, Candidate Experience & Responsible Use

·        Integrating AI with structured interviews and assessments (consistency controls)

·        Candidate experience: transparency, communications cadence, and service recovery

·        Responsible AI: bias risks, disparate impact concepts, and mitigation methods

·        Privacy and compliance: consent, data minimization, retention, and access controls

·        Case study: Addressing an AI screening fairness complaint (investigation + control improvements)

Day 5: Governance, Metrics & Implementation Roadmap

·        TA governance for AI: roles, decision rights, approvals, and audit trails

·        Controls and assurance: validation testing, monitoring drift, and documentation standards

·        Metrics: funnel conversion, time-to-hire, quality-of-hire signals, DEI indicators, and candidate satisfaction

·        Pilot design: success criteria, guardrails, training, and change adoption