Advanced AI for HR and Talent Management Training Course – Casablanca, 27 Sep – 8 Oct 2027

AI for HR and Talent Management is a 2-week advanced training course for HR directors, talent acquisition leads and people analytics managers who will build an AI-enabled talent strategy for their organisation. Machine learning now shapes who is hired, developed and retained, so HR leaders need the judgement to direct these models and audit their fairness. Jupiter Prime Training runs this AI for HR and Talent Management course for senior people leaders.

Why AI for HR and Talent Management Matters Now

Three forces shape this subject today: a shift from job titles to skills as the currency of hiring, recruitment and HR software vendors embedding prediction into every screening step, and employees who expect people decisions about them to be explainable. An organisation that steers these models well redeploys talent faster and keeps scarce specialists; one that lets them run unchecked risks biased shortlists, lost trust and legal exposure. The course rests on ISO/IEC 42001, the AI management system standard, and the NIST AI Risk Management Framework (NIST AI RMF).

Who Gains Most from AI for HR and Talent Management

• Leaders accountable for hiring speed, quality of hire and candidate experience
• Leaders who own workforce planning, succession and organisation design
• Leaders responsible for people analytics, HR data and reporting to executives
• Leaders who set reward, retention and employee experience priorities
• Leaders who oversee HR technology selection and vendor contracts

An HR leadership team that sends its talent acquisition, analytics and HR business partner leads together gains most, because the attrition, skills and fairness models only work when those functions agree on data and decision rights.

AI for HR and Talent Management Capabilities

Participants move from reading vendor dashboards to questioning, specifying and governing the talent models behind them.

• Assess attrition, quality-of-hire and succession models for accuracy and fitness for use — HR leadership investment choices
• Design a skills taxonomy and inference approach that supports internal mobility — workforce planning and redeployment
• Test hiring and promotion algorithms for adverse impact and proxy bias — talent acquisition and employee relations
• Specify vendor due diligence questions for HR AI tools — procurement and contract owners
• Build explainability and employee data notices for automated people decisions — employees and works councils
• Align HR AI use with an AI management system and risk framework — executive committee oversight

Key AI for HR and Talent Management Terms

• People analytics — the use of employee and workforce data to explain and predict outcomes such as performance, engagement and turnover.
• Attrition model — a machine learning model that estimates the likelihood of an employee leaving within a set period.
• Skills inference — deriving the capabilities a person likely holds from job history, projects and learning records rather than self-declaration.
• Adverse impact ratio — the selection rate of one group divided by that of the most selected group, used to flag possible bias.
• Proxy variable — a data field, such as postcode or university, that indirectly encodes a protected characteristic in a model.
• Internal talent marketplace — a platform matching employees to roles, projects and gigs inside the organisation using skills data.

AI for HR and Talent Management Day by Day

Day 1: Talent Markets and Algorithmic Hiring Forces

• Skills-Based Hiring Shift — Rethinking Role Requirements Around Capabilities
• Automation Exposure Mapping — Which HR Tasks Machine Learning Changes
• Employee Expectations of Algorithmic Fairness — Trust in People Decisions
• Workforce Data Inventory — Sources Available for Talent Models
• HR Technology Stack Audit — Gaps Blocking Predictive Talent Use

Day 2: People Analytics Foundations for HR Leaders

• Descriptive to Predictive Analytics Ladder — Setting HR Analytics Ambition
• HRIS and ATS Data Quality Checks — Trusting Talent Model Inputs
• Feature Selection From Tenure, Performance and Pay — Model Input Choices
• Correlation Versus Causation in Engagement Data — Avoiding False Talent Conclusions
• People Analytics Question Framing — Linking Models to Board Concerns

Day 3: Machine Learning Methods for Talent Decisions

• Classification Models for Attrition Risk — Flagging Employees Likely to Leave
• Regression for Pay and Performance Drivers — Explaining Reward Outcomes
• Clustering Employee Segments — Tailoring Development and Retention Offers
• Natural Language Processing of Survey Comments — Surfacing Engagement Themes
• Model Accuracy, Precision and Recall — Judging Talent Predictions Fit for Use

Day 4: Strategic Workforce Planning With AI

• Demand Forecasting From Business Drivers — Sizing Future Headcount Needs
• Supply Modelling of Internal Mobility — Predicting Talent Pipeline Gaps
• Scenario Simulation for Restructuring — Comparing Build, Buy and Borrow Options
• Succession Risk Scoring — Prioritising Critical Role Coverage
• Workforce Plan Dashboards — Communicating Capacity Gaps to Executives

Day 5: Skills Mapping and Internal Talent Marketplaces

• Skills Taxonomy and Ontology Design — Common Language for Capabilities
• Skills Inference From Job Histories — Detecting Hidden Employee Capabilities
• Adjacent Skills Matching — Redeploying Staff Into Emerging Roles
• Internal Gig and Project Marketplace Models — Opening Stretch Assignments
• Learning Path Recommendation Engines — Closing Priority Skills Gaps

Day 6: AI-Enabled Candidate Sourcing and Screening

• Job Description Language Analysis — Removing Exclusionary Wording
• CV Parsing and Candidate Ranking Models — Shortlisting Decisions at Scale
• Generative AI Interview Question Design — Structured Assessment Consistency
• Conversational Recruiting Assistants — Candidate Experience and Hand-Off Rules
• Quality-of-Hire Prediction — Linking Selection Signals to Later Performance

Day 7: Retention Prediction and Employee Experience

• Survival Analysis of Tenure — Timing Retention Interventions
• Flight Risk Drivers Explained With SHAP Values — Manager Conversations
• Sentiment Trends From Pulse Surveys — Early Warning on Team Morale
• Next-Best-Action Retention Recommendations — Choosing Interventions per Segment
• Intervention Testing With Control Groups — Proving Retention Effects

Day 8: Bias Auditing of Hiring and Promotion Algorithms

• Adverse Impact Ratio Testing — Detecting Disparate Selection Outcomes
• Proxy Variable Detection — Removing Hidden Protected Attribute Signals
• Fairness Metrics Trade-Offs — Choosing Parity Definitions for Talent Models
• Vendor Algorithm Due Diligence Questionnaire — Procurement of HR AI Tools
• Human Review Checkpoints — Overriding Automated Candidate Rejections

Day 9: Employee Data Ethics and AI Oversight

• ISO/IEC 42001 AI Management System — Structuring HR AI Accountability
• NIST AI RMF Map, Measure, Manage Functions — Talent Model Risk Controls
• Employee Data Minimisation and Consent Notices — Privacy in People Analytics
• Explainability Statements for Employees — Transparency About Automated Decisions
• HR AI Use Register — Tracking Models, Owners and Review Dates

Day 10: Practising the AI-Enabled Talent Strategy

• Attrition Model Review Applied to a Live Business Unit — Retention Priorities
• Skills Gap Map Applied to a Critical Job Family — Reskilling Choices
• Bias Audit Applied to a Current Screening Tool — Fairness Remediation
• Workforce Scenario Applied to a Growth Plan — Hiring Sequence Decisions
• AI-Enabled Talent Strategy Assembly — Roadmap, Controls and Executive Sponsorship

Applying AI for HR and Talent Management After the Course

• Run an adverse impact ratio test on the last two hiring cycles of one screening tool and present remediation options to the head of talent acquisition.
• Pilot an attrition risk model with SHAP-based explanations in one business unit and agree manager conversation guides with its HR business partner.

AI for HR and Talent Management Questions (FAQ)

What is AI for HR and talent management, and why does it matter now?

It is the use of machine learning to plan the workforce, map skills, screen candidates and predict retention. It matters now because these models already sit inside most HR software, and leaders who cannot question them inherit their errors and biases.

Which roles gain most from AI for HR and talent management?

Senior HR leaders gain most: directors, talent acquisition heads and people analytics managers. You benefit if you approve HR technology, own workforce planning or answer to executives for fair, explainable decisions about hiring, promotion and retention.

How does advanced AI for HR and talent management differ from a general AI for business course?

It goes deeper into people decisions specifically. A general AI course covers use cases across functions, while this course works through attrition models, skills inference, hiring algorithm bias audits and employee data ethics in detail across two weeks.

What do you bring back from AI for HR and talent management training?

You bring back an AI-enabled talent strategy. It covers retention priorities, a skills gap map, a bias audit of a screening tool, workforce scenarios and the controls that keep employee data use fair and transparent.

AI for HR and Talent Management: The Way Forward

The AI-enabled talent strategy first informs which talent model to fund and which to retire. HR directors use it with finance and business unit heads to sequence hiring and reskilling, while analytics leads use it to schedule fairness audits. After its first use, revisit the skills taxonomy, as emerging roles quickly change which capabilities the models should look for.