About This Course
Artificial intelligence is no longer a distant concept reserved for technology companies — it is actively reshaping how organisations attract talent, manage performance, develop employees, and make people decisions. From intelligent applicant tracking systems and predictive attrition models to AI-powered learning platforms and automated onboarding workflows, HR functions that embrace these technologies are gaining measurable competitive advantages in speed, accuracy, and employee experience.
Yet many HR professionals feel underprepared to evaluate, implement, or govern these tools. Questions around algorithmic bias, data privacy, change management, and the appropriate boundaries of automation continue to create uncertainty. This programme bridges that gap by combining conceptual grounding with practical application, ensuring participants can make informed, confident decisions about AI adoption within their HR context.
TrainingComplex designed this course for HR practitioners who want to move beyond awareness and into action. Over five intensive days, participants examine real-world AI use cases across recruitment, onboarding, performance management, learning and development, workforce planning, and employee engagement. Each module balances technical understanding with strategic and ethical considerations, reflecting the dual demands placed on modern HR leaders.
Participants leave with a working knowledge of the AI landscape in HR, the ability to critically assess vendor solutions, and a personalised implementation roadmap they can present to senior stakeholders. Whether your organisation is just beginning its AI journey or looking to optimise an existing digital HR strategy, this programme provides the clarity, tools, and confidence to lead that transformation.
Who Should Attend
- HR Managers and HR Business Partners seeking to modernise people practices
- Talent Acquisition Specialists looking to leverage AI in sourcing, screening, and hiring
- Learning and Development Professionals exploring AI-powered training personalisation
- HR Directors and CHROs responsible for digital HR strategy and investment decisions
- People Analytics Professionals building or expanding workforce data capabilities
- Organisational Development Consultants advising clients on HR transformation
- HR Technology Project Managers overseeing HRIS and automation implementations
- Total Rewards and Employee Experience Managers integrating digital engagement tools
Learning Objectives
- Explain the fundamentals of artificial intelligence, machine learning, and automation as they apply to HR functions
- Identify and evaluate AI-powered tools across the full employee lifecycle, from recruitment to offboarding
- Apply AI-driven sourcing, screening, and candidate assessment techniques to improve talent acquisition outcomes
- Design automated onboarding workflows that enhance new-hire experience and reduce administrative burden
- Interpret people analytics dashboards and predictive models to support data-driven workforce decisions
- Assess algorithmic bias risks in HR AI systems and apply mitigation strategies aligned with ethical standards
- Navigate data privacy regulations and governance requirements relevant to AI use in HR contexts
- Develop a business case for HR AI adoption tailored to their organisation's strategic priorities
- Create a phased implementation roadmap for introducing AI and automation into an HR function
- Communicate AI-driven HR initiatives effectively to senior leadership, employees, and other stakeholders
Course Modules
Module 1: The AI and Automation Landscape in HR
- Defining artificial intelligence, machine learning, and automation in plain language
- The evolution of HR technology from HRIS to intelligent systems
- Current state of AI adoption across HR functions globally
- Key drivers and barriers to AI implementation in people management
- Mapping AI capabilities to the employee lifecycle
- Setting realistic expectations: what AI can and cannot do in HR
Module 2: AI in Talent Sourcing and Candidate Attraction
- How AI algorithms surface and rank candidates across job boards and social platforms
- Programmatic job advertising and audience targeting using machine learning
- AI-powered employer branding and personalised candidate communication
- Chatbots and conversational AI for candidate engagement and FAQ handling
- Ethical sourcing: avoiding bias in AI-driven talent pools
- Measuring sourcing effectiveness with AI-generated recruitment analytics
Module 3: Intelligent Screening, Assessment, and Selection
- Resume parsing and automated CV screening: how it works and its limitations
- AI-driven psychometric and cognitive assessments in the hiring process
- Video interview analysis tools: sentiment, language, and behavioural signals
- Structured vs. unstructured data in candidate evaluation models
- Audit frameworks for detecting and correcting screening bias
- Compliance considerations when using AI in hiring decisions
Module 4: Automating Onboarding and Employee Experience
- Mapping the onboarding journey for automation opportunities
- Digital document management, e-signatures, and compliance workflow automation
- AI chatbots as onboarding guides and HR helpdesk assistants
- Personalised onboarding pathways driven by role, location, and learning profile
- Measuring onboarding effectiveness with automated pulse surveys and analytics
- Integrating onboarding automation with HRIS and collaboration platforms
Module 5: People Analytics and Workforce Intelligence
- Building a people analytics function: data sources, architecture, and governance
- Key HR metrics vs. predictive analytics: understanding the distinction
- Attrition prediction models: inputs, outputs, and responsible use
- Workforce planning scenarios powered by demand forecasting algorithms
- Data visualisation tools and dashboard design for HR decision-makers
- Translating analytics insights into actionable people strategies
Module 6: AI in Learning and Development
- How AI personalises learning pathways based on role, skills gaps, and behaviour
- Intelligent content curation and recommendation engines in LMS platforms
- Natural language processing tools for microlearning content generation
- Skills taxonomy management and AI-driven competency gap analysis
- Measuring L&D ROI using automated learning analytics and performance correlation
- Ethical and inclusivity considerations in AI-personalised learning programmes
Module 7: Performance Management and Continuous Feedback Systems
- Shifting from annual reviews to continuous AI-supported performance conversations
- Sentiment analysis of employee feedback to identify engagement trends
- Goal alignment tools and OKR platforms with AI-assisted tracking
- Real-time coaching nudges and manager guidance delivered by AI systems
- Fairness and transparency in AI-informed performance ratings
- Integrating performance data with compensation and succession planning systems
Module 8: Algorithmic Bias, Ethics, and Responsible AI in HR
- Sources and types of bias in HR AI systems: historical, sampling, and proxy bias
- The business and legal case for ethical AI in people management
- Fairness metrics and bias testing methodologies applicable to HR contexts
- Designing human-in-the-loop processes to maintain accountability
- Building an internal AI ethics policy for HR applications
- Stakeholder communication strategies around responsible AI use
Module 9: Data Privacy, Legal Compliance, and AI Governance
- Key data protection regulations affecting HR AI: GDPR, Kenya Data Protection Act, and regional equivalents
- Employee consent frameworks for AI-driven data collection and processing
- Data minimisation principles and retention policies in HR AI systems
- Vendor due diligence: assessing AI supplier data security and compliance posture
- Building an HR AI governance committee: roles, responsibilities, and review cycles
- Documenting AI decision trails for audit and regulatory accountability
Module 10: Building the Business Case and Implementation Roadmap
- Conducting an AI readiness assessment across people, process, and technology dimensions
- Prioritising HR AI use cases by impact, feasibility, and strategic alignment
- Structuring a compelling business case for executive sponsorship and budget approval
- Designing a phased implementation roadmap with milestones, KPIs, and risk controls
- Change management strategies to drive HR team and employee adoption of AI tools
- Capstone project presentations: participant roadmaps reviewed and workshopped by peers
Requirements & Prerequisites
Participants should have at least two years of experience working in an HR, talent, or people-related role. A basic familiarity with HR processes such as recruitment, onboarding, performance management, or learning and development is expected. No prior knowledge of artificial intelligence, data science, or programming is required — the course is designed to be accessible to HR professionals without a technical background.
Training Methodology
- Live demonstrations of leading HR AI platforms including applicant tracking systems, chatbot builders, and people analytics dashboards
- Case study analysis of real organisational AI in HR implementations drawn from sectors such as financial services, manufacturing, and the public sector
- Group workshops where participants evaluate fictional AI vendor proposals against a structured criteria framework
- Bias audit exercises using sample recruitment datasets to identify and address algorithmic discrimination patterns
- Role-play scenarios simulating stakeholder conversations between HR leaders, C-suite executives, and employee representatives on AI adoption
- Hands-on building of a simple automated HR workflow using a no-code automation tool such as Zapier or Microsoft Power Automate
- Peer-learning discussions anchored to participants' own organisational contexts and HR technology maturity levels
- Individual reflection journalling at the close of each day to consolidate key insights and identify personal action points
- A capstone business case project developed incrementally across the five days and presented to a panel on the final afternoon
Organisational Benefits
- Accelerated talent acquisition through AI-assisted sourcing and screening, reducing time-to-hire and cost-per-hire
- Improved quality of hire supported by data-driven candidate assessment and predictive success modelling
- Reduced administrative burden on HR teams through intelligent automation of repetitive transactional tasks
- Stronger employee retention driven by early attrition prediction and proactive engagement interventions
- More personalised and effective learning experiences delivered through AI-powered L&D platforms
- Enhanced workforce planning accuracy through predictive analytics and scenario modelling capabilities
- Greater compliance confidence through structured AI governance frameworks and bias auditing practices
- Higher HR function credibility with senior leadership through evidence-based, analytics-informed people strategies
- Faster digital HR transformation supported by staff who can evaluate vendors, manage implementations, and drive adoption
- Improved employee experience across key lifecycle touchpoints through intelligent, responsive HR technology
Where Is Artificial Intelligence and Automation in HR Training Offered?
This course runs in-person across: Nairobi, Johannesburg, Kigali, Kampala, Cape Town, Lagos, Abuja, Riyadh, Dar es Salaam, Accra, Mombasa, Naivasha, Dubai, Nakuru, Addis Ababa, Kisumu, Pretoria, Zanzibar, Arusha, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Nairobi, Kenya |
In-Person |
5 Oct 2026 – 9 Oct 2026 |
KES 105,000 |
| Johannesburg, South Africa |
In-Person |
5 Oct 2026 – 9 Oct 2026 |
USD 3,500 |
| Kigali, Rwanda |
In-Person |
12 Oct 2026 – 16 Oct 2026 |
USD 1,900 |
| Kampala, Uganda |
In-Person |
12 Oct 2026 – 16 Oct 2026 |
USD 1,900 |
| Cape Town, South Africa |
In-Person |
19 Oct 2026 – 23 Oct 2026 |
USD 3,800 |
| Lagos, Nigeria |
In-Person |
19 Oct 2026 – 23 Oct 2026 |
USD 2,500 |
| Abuja, Nigeria |
In-Person |
19 Oct 2026 – 23 Oct 2026 |
USD 2,800 |
| Riyadh, Saudi Arabia |
In-Person |
19 Oct 2026 – 23 Oct 2026 |
USD 4,500 |
| Dar es Salaam, Tanzania |
In-Person |
19 Oct 2026 – 23 Oct 2026 |
USD 2,300 |
| Accra, Ghana |
In-Person |
19 Oct 2026 – 23 Oct 2026 |
USD 3,800 |
| Mombasa, Kenya |
In-Person |
19 Oct 2026 – 23 Oct 2026 |
KES 120,000 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 23 Oct 2026 |
KES 115,000 |
| Live Virtual |
Online |
24 Oct 2026 – 15 Nov 2026 |
KES 80,000 |
| Dubai, United Arab Emirates |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 4,100 |
| Nakuru, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
+237 more dates — view live schedule →
Frequently Asked Questions
Do I need a background in technology or data science to attend this course?
No technical background is required. The programme is specifically designed for HR professionals and uses plain-language explanations throughout. You will develop a strong conceptual and practical understanding of AI in HR without needing to write code or work with raw data.
Will I receive a certificate upon completing the course?
Yes. Participants who attend all five days and complete the capstone business case project will receive a Certificate of Completion from TrainingComplex. The certificate specifies the course title, duration, and learning outcomes covered.
What tools or platforms will we use during the practical sessions?
The course incorporates live demonstrations and hands-on exercises using widely available tools including Microsoft Power Automate, sample HR analytics dashboards, and leading ATS and LMS platforms. All exercises are conducted in guided, sandbox or demonstration environments so no organisational data or licences are required.
Is this course suitable for someone whose organisation has not yet adopted any AI tools?
Absolutely. The programme is structured to be valuable regardless of your organisation's current AI maturity. Early modules build foundational understanding, and the capstone project helps you develop a realistic roadmap for introducing AI tools at a pace and scale appropriate to your context.
How is the hybrid format structured?
In-person participants engage in face-to-face workshops, group exercises, and peer discussions at the training venue, while online participants join via a live video platform and participate in all activities in real time. Both groups access the same facilitators, materials, and breakout sessions, ensuring an equivalent learning experience.
