Artificial Intelligence in Human Resources Training Course

Duration 10 days 16 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends HR Directors, HR Business Partners and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-HM-090

Before you join: Participants should understand core HR processes such as recruitment, performance management, learning, employee relations or workforce planning.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Artificial intelligence is changing how HR teams source candidates, screen applications, answer employee questions, identify skills, predict workforce needs and support learning. The opportunity is substantial, but HR decisions affect livelihoods, privacy, equality and employee trust. HR leaders need to distinguish useful AI applications from unsuitable ones, assess vendor claims, control data quality, test for unfair outcomes and retain accountable human decision-making.

This course examines practical AI use across the employee lifecycle: workforce planning, talent acquisition, onboarding, learning, performance, employee listening, service delivery and retention. Participants learn the core concepts behind machine learning, generative AI and predictive models without requiring programming skills. They apply NIST AI RMF, ISO/IEC 42001 concepts, GDPR principles and the Uniform Guidelines on Employee Selection Procedures to HR use cases. The course also covers prompt design, data governance, bias testing, model monitoring, vendor due diligence and change management.

Instructor-led sessions combine short technical briefings with HR cases from banking, public services, multinational employers and regulated industries. Teams examine candidate-screening, attrition-risk, HR chatbot and skills-inference scenarios, then develop controls and implementation choices. Participants leave with an AI-enabled HR use-case business case, risk assessment, governance model, vendor evaluation checklist and implementation roadmap suitable for adaptation within their employer.

The course suits HR professionals, people analytics specialists, HR technology leaders, risk managers and business leaders who sponsor HR automation. It is particularly relevant where organisations are procuring AI-enabled HR platforms, expanding people analytics or setting policies for generative AI use by HR teams.

What you will be able to do

  • Assess HR AI use cases using value, feasibility, risk and readiness criteria
  • Differentiate machine learning, generative AI, rules engines and predictive analytics applications
  • Create AI prompts for HR drafting, analysis and employee-service tasks
  • Evaluate HR datasets for quality, privacy, representativeness and lawful processing
  • Test selection and workforce models for bias using adverse impact analysis
  • Apply NIST AI RMF controls to an HR AI governance model
  • Conduct vendor due diligence for AI-enabled HR technology procurement
  • Produce an HR AI implementation business case, control plan and roadmap

Benefits of attending

For you

  • Confidence to challenge unsupported AI vendor claims
  • Practical methods for assessing HR automation proposals
  • A defensible approach to employee-data and bias risks
  • Reusable prompts for HR communications and analysis
  • Evidence for leading AI-related HR change discussions

For your organisation

  • Better prioritisation of HR AI investments against measurable workforce needs
  • Reduced exposure to discriminatory selection and privacy failures
  • Clearer accountability for AI-assisted HR decisions
  • Stronger procurement requirements for HR technology suppliers
  • Faster adoption of approved AI tools through defined operating controls

Course modules

16 modules over 10 days

Participants can explain major AI approaches and identify their appropriate HR applications.

  • Artificial intelligence, machine learning, generative AI and automation distinguished clearly
  • Rules-based systems compared with supervised and unsupervised learning models
  • Natural language processing used for HR text classification and retrieval
  • Computer vision applications assessed cautiously for workplace and recruitment use
  • Training data, features, labels and predictions explained through HR examples
  • Human judgement retained within AI-assisted employment decision workflows

Participants can prioritise HR AI opportunities against business value, feasibility and risk.

  • Employee lifecycle mapped from workforce planning through exit management
  • Value-feasibility-risk matrix used to rank HR AI opportunities
  • Problem statements framed before selecting technology or data solutions
  • Baseline service metrics established for time, quality, cost and experience
  • Use cases separated between decision support and decision automation
  • Benefit hypotheses linked to measurable HR and business outcomes

Participants can assess whether people data is fit for a proposed AI use case.

  • Data inventory records HR sources, owners, fields and retention periods
  • Completeness, accuracy, timeliness and consistency tested before model development
  • Historical recruitment data examined for inherited process and demographic bias
  • Data lineage documents transformations from source systems to model outputs
  • Missing values, duplicate records and inconsistent job codes diagnosed systematically
  • Minimum viable dataset defined for a selected HR AI use case

Participants can use generative AI appropriately for bounded HR drafting and analysis tasks.

  • Prompt structure combines role, task, context, constraints and output format
  • Few-shot prompting improves consistency for recurring HR content generation
  • Job descriptions revised using inclusive language and approved competency criteria
  • Policy summaries checked against source documents before publication
  • Hallucination risks identified in employment advice and regulatory interpretations
  • Human review criteria set for generated communications and analytical outputs

Participants can define responsible AI applications for workforce demand, supply and skills decisions.

  • Demand forecasting combines business drivers, headcount history and scenario assumptions
  • Skills taxonomy links roles, capabilities, proficiency levels and learning pathways
  • Skills inference methods assessed against accuracy, transparency and employee consent
  • Scenario planning compares hiring, redeployment, outsourcing and automation options
  • Internal mobility recommendations reviewed for explainability and equal access
  • Workforce planning outputs translated into accountable talent actions

Participants can evaluate AI-supported sourcing, screening and candidate engagement processes.

  • Recruitment funnel metrics identify bottlenecks before introducing AI tools
  • Semantic search matches candidate evidence against defined job requirements
  • Automated screening rules documented with recruiter review and exception handling
  • Candidate ranking outputs separated from final employment decision authority
  • Conversational assistants provide status updates, scheduling and frequently asked questions
  • Selection validity considered alongside speed, cost and candidate experience

Participants can identify, measure and respond to unfair outcomes from HR AI systems.

  • Protected characteristics identified under applicable employment and equality requirements
  • Adverse impact ratio calculated using the four-fifths rule
  • Selection rates compared across demographic groups and decision stages
  • Proxy variables examined for indirect discrimination and unjustified exclusion
  • Fairness metrics interpreted with legal, statistical and operational context
  • Remediation options include redesign, threshold changes, review and withdrawal

Participants can apply privacy principles to HR AI data collection and processing decisions.

  • Purpose limitation defines permitted uses of employee and candidate data
  • Data minimisation removes fields unnecessary for stated AI objectives
  • Lawful basis assessed before processing sensitive workforce information
  • Privacy impact assessment records risks, controls and residual exposure
  • Retention schedules align model data, outputs and audit records
  • Employee notices explain automated processing, rights and contact routes

Participants can design governance roles, policies and controls for HR AI use.

  • NIST AI RMF functions applied to HR AI governance decisions
  • ISO/IEC 42001 concepts translated into HR management system responsibilities
  • RACI matrix assigns ownership for data, models, approvals and monitoring
  • Acceptable-use policy distinguishes approved, restricted and prohibited HR AI activities
  • Human oversight triggers defined for high-impact employment decisions
  • Incident escalation process covers errors, complaints, harm and system suspension

Participants can conduct structured due diligence on AI-enabled HR technology suppliers.

  • Vendor claims tested against documented functionality, evidence and operating constraints
  • Procurement questionnaire requests training data provenance and model documentation
  • Validation evidence reviewed for relevance to intended employee populations
  • Contract clauses address data ownership, confidentiality, audit rights and liability
  • Service-level measures define uptime, response times, accuracy and support
  • Exit plans protect HR data portability and operational continuity

Participants can design AI-supported HR services that preserve accessibility and trust.

  • Employee journey mapping identifies service friction and suitable automation points
  • HR chatbots scoped for policy guidance, case routing and routine transactions
  • Knowledge bases structured for retrieval accuracy, version control and ownership
  • Escalation pathways protect employees facing sensitive or complex matters
  • Accessibility requirements considered for language, disability and digital access
  • Service quality measured through resolution, accuracy, satisfaction and abandonment rates

Participants can assess predictive HR models without treating predictions as employment facts.

  • Attrition models distinguish correlation, causation and actionable intervention factors
  • Model features screened for sensitive attributes and inappropriate proxies
  • Precision, recall and false-positive rates interpreted for HR decisions
  • Performance predictions restricted where feedback quality and rating bias persist
  • Intervention design avoids punitive action against predicted employee behaviour
  • Monitoring detects model drift after workforce, policy or market changes

Participants can plan a controlled rollout of an approved HR AI use case.

  • Pilot scope defines users, process boundaries, duration and success measures
  • Stakeholder mapping identifies affected employees, managers, unions and regulators
  • Change impact assessment addresses roles, skills, workload and decision rights
  • Training plan prepares HR teams for prompts, review and escalation
  • Communication messages explain purpose, safeguards, limitations and employee support
  • Go-live criteria require testing evidence, approvals and operational readiness

Participants can monitor HR AI performance using balanced operational, people and risk measures.

  • Benefits realisation plan links AI outputs to agreed HR performance measures
  • Balanced scorecard tracks efficiency, quality, fairness, adoption and trust
  • Control indicators monitor override rates, complaints, errors and exceptions
  • Model performance thresholds trigger review, retraining, restriction or retirement
  • User feedback loops capture recruiter, manager, employee and candidate experience
  • Audit trail requirements preserve decisions, inputs, approvals and changes

Participants can produce an operating model that governs everyday HR use of AI.

  • Operating model defines central, federated and shared AI responsibilities
  • HR AI policy covers permitted tools, data handling and approval routes
  • Use-case intake process captures purpose, users, data and risk classification
  • Control library maps risks to preventive, detective and corrective measures
  • Decision register records approvals, conditions, owners and review dates
  • Quarterly governance review examines performance, incidents, legal changes and priorities

Participants can build, review and present a complete HR AI use-case proposal with controls, implementation plan and success measures.

  • Business problem defined with affected groups, baseline evidence and target outcomes
  • Use-case canvas specifies process, users, data, technology and decision authority
  • Risk assessment covers fairness, privacy, security, transparency and employee impact
  • Governance controls assign approvals, human review, monitoring and incident response
  • Implementation roadmap sequences pilot, evaluation, rollout and continuous review
  • Peer review challenges assumptions, evidence, safeguards and expected benefits

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. HR AI use-case prioritisation matrix
  2. People-data readiness assessment template
  3. HR AI risk and control register
  4. AI vendor due-diligence checklist
  5. Generative AI prompt library for HR
  6. HR AI implementation business-case pack

Who should attend

  • HR Directors — sponsors AI priorities, policy and investment decisions
  • HR Business Partners — advises leaders on responsible workforce applications
  • People Analytics Managers — develops models, metrics and data controls
  • Talent Acquisition Leaders — assesses AI sourcing and selection tools
  • HR Technology Managers — procures, configures and governs HR platforms
  • Risk and Compliance Managers — reviews employment, privacy and model risks

Skills and tools covered

HR AI governancePeople data analysisBias risk assessmentAI vendor evaluationPrompt designImplementation planning

Tools and standards

NIST AI Risk Management Framework, ISO/IEC 42001, GDPR, Uniform Guidelines on Employee Selection Procedures

How the course runs

Training methodology

The course uses short instructor briefings to establish AI, employment and governance concepts, followed by case analysis, group discussion, structured simulations and application planning. Participants assess realistic HR scenarios, including automated screening, employee self-service assistants, retention prediction and skills matching. They work with practical templates for risk assessment, prompt review, vendor due diligence and implementation planning. Facilitated peer review helps participants compare choices across industries, regulatory contexts and HR operating models.

Requirements and prerequisites

Participants should understand core HR processes such as recruitment, performance management, learning, employee relations or workforce planning. Familiarity with HR information systems, spreadsheets and basic HR metrics is helpful. Participants should be able to discuss how their organisation collects and uses employee or candidate data. No programming, data science, mathematics or prior AI implementation experience is required. The course explains technical concepts in HR terms, while expecting participants to engage critically with data, policy, risk and operational decisions.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants need practical familiarity with one or more HR processes and a basic understanding of how their organisation manages employee data. Programming, statistics and prior AI project experience are not required.

Bring a laptop if attending in person and, where permitted, an anonymised HR process problem or proposed AI use case. Participants should not bring identifiable employee, candidate or confidential organisational data.

It is intended for HR leaders, business partners, people analytics teams, talent acquisition leaders, HR technology managers and risk professionals. It is most useful for people involved in selecting, governing or using AI-enabled HR tools.

People analytics courses focus mainly on HR data analysis and reporting, while HR systems courses focus on platform configuration and process administration. This course concentrates on AI applications, employment risks, governance controls, procurement and implementation decisions.

Participants can use the templates to assess a proposed HR AI tool, improve an existing workflow or prepare a controlled pilot. The capstone proposal provides a structured starting point for discussions with HR, legal, technology, procurement and employee representatives.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The first week establishes AI concepts and builds practical HR use cases; the second week addresses governance, vendor review, implementation, monitoring and the capstone presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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