AI-Powered Talent Acquisition Masterclass
You’re under pressure. Hiring is slower than ever, quality is slipping, and leadership is asking for faster results with tighter budgets. You’re not just filling roles - you’re expected to predict cultural fit, assess potential, and scale talent pipelines with AI, but without a clear roadmap. Legacy recruitment methods are failing. Manual screening wastes hours. Biases slip through. High-potential candidates get overlooked. You know AI can fix this, but most training is too technical, too vague, or too theoretical to apply on Monday morning. The AI-Powered Talent Acquisition Masterclass is not another generic course. It’s the only structured, practitioner-led system that takes you from overwhelmed to in control - transforming how you source, assess, and secure elite talent using intelligent automation, ethical AI frameworks, and data-driven decision making. In just 21 days, you’ll build a board-ready talent strategy powered by AI, complete with an implementation plan, compliance safeguards, and measurable KPIs. You’ll stop guessing and start delivering faster hires, 40% shorter time-to-fill, and higher offer acceptance rates - all while reducing bias and operational load. Take Sarah Lim, Talent Acquisition Lead at a global fintech firm. After completing this masterclass, she automated 65% of her screening workload, reduced hiring bias by 52% using AI calibration tools, and presented a successful AI rollout plan to her CHRO - leading to a promotion within 90 days. This isn’t about learning AI in isolation. It’s about owning the future of talent. Here’s how this course is structured to help you get there.Course Format & Delivery Details The AI-Powered Talent Acquisition Masterclass is designed for demanding professionals who need clarity, flexibility, and real impact - without sacrificing credibility or control. Self-Paced. Immediate Online Access. Lifetime Updates.
This is an entirely self-paced, on-demand learning experience. Enroll once, access forever. No fixed start dates, no weekly schedules, no deadlines. Learn at your pace, on your terms, from any device. Most learners complete the core curriculum in 3 to 4 weeks with 4-6 hours per week. Many apply their first AI-driven screening workflow within 7 days. Earlier results are possible depending on your role and organisational setup. You receive lifetime access to all materials, including all future content upgrades at no additional cost. Every framework, tool, and AI benchmark will be updated as the landscape evolves - your knowledge stays current, automatically. 24/7 Global Access. Fully Mobile-Friendly.
Access your course materials anytime, anywhere. Whether you’re on a desktop in HQ or reviewing workflows on your phone during a commute, the platform adapts seamlessly. Sync progress across devices, track milestones, and resume exactly where you left off. Direct Instructor Guidance & Real-World Support
You’re not alone. Throughout the course, you’ll have access to structured support from certified talent strategists with over a decade of combined experience in AI integration and global workforce planning. Ask questions, submit use cases, and receive actionable feedback on your strategy development. Certificate of Completion - Issued by The Art of Service
Upon successful completion, you will earn a globally recognised Certificate of Completion issued by The Art of Service, a leader in professional certification and enterprise capability development. This credential demonstrates mastery of AI-integrated talent acquisition and is shareable on LinkedIn, resumes, and performance reviews. Trusted by HR Leaders. Built for Real Impact.
- This works even if you’re not technical. We translate AI concepts into clear, role-aligned actions for recruiters, sourcers, HR business partners, and talent directors.
- This works even if your organisation hasn’t adopted AI yet. You’ll build a compliant, low-risk pilot project ready for internal approval.
- This works even if you’ve tried AI tools before. We show you how to move beyond automation gimmicks to strategic workforce transformation.
Don’t just take our word for it. One learner at a Fortune 500 pharma company used the course framework to design an AI-augmented high-volume hiring system for clinical trial staff. After rollout, their regional hiring team reduced screening time by 58% and increased diversity in shortlisted candidates by 33%. Zero Risk. 100% Satisfaction Guarantee.
We offer a full refund if you’re not satisfied with the value you receive. If after completing the first three modules you don’t feel more confident, more strategic, and more in control of your talent outcomes, simply request a refund. No questions, no hassle. Transparent Pricing. No Hidden Fees.
The enrolment fee includes full access to all materials, resources, templates, case studies, and your Certificate of Completion. No recurring charges. No surprise costs. One straightforward payment. We accept all major payment methods, including Visa, Mastercard, and PayPal. After enrolment, you’ll receive a confirmation email. Your access details and login credentials will be sent in a separate email once your course materials are fully configured - ensuring a smooth, reliable onboarding experience.
Module 1: Foundations of AI in Talent Acquisition - Understanding the shift from traditional to AI-enhanced hiring
- Core definitions: machine learning, NLP, predictive analytics in recruitment
- Common misconceptions and myths about AI in HR
- AI maturity models for talent teams
- The ethical imperative: fairness, transparency, and accountability
- Key roles in AI-powered talent acquisition
- How AI impacts candidate experience
- Case study: AI transformation at a global logistics firm
- Mapping AI capabilities to business outcomes
- Key risks and mitigation strategies
Module 2: Strategic Frameworks for AI Integration - The 5-phase AI adoption roadmap
- Building executive alignment and securing stakeholder buy-in
- Developing a talent AI vision statement
- Aligning AI initiatives with DEI goals
- Creating a pilot program charter
- Setting measurable KPIs for AI recruitment success
- Designing a phased rollout plan
- Stakeholder communication templates
- Change management principles for HR teams
- ROI assessment models for AI tools
Module 3: AI-Powered Sourcing & Candidate Discovery - Automated candidate sourcing using AI search logic
- Beyond Boolean: semantic search and contextual matching
- Profile enrichment using AI-driven public data analysis
- Passive candidate identification frameworks
- AI tools for diversity sourcing
- Reducing geographic and gender bias in sourcing
- Building candidate persona clusters
- Automated outreach sequencing principles
- Personalisation at scale using dynamic content
- Tracking sourcing pipeline health with AI
Module 4: Intelligent Screening & Shortlisting - Automated resume parsing accuracy benchmarks
- Extracting experience, skills, and competencies
- Matching algorithms vs human judgment
- Configuring relevance thresholds for screening
- Customising AI filters by role complexity
- Integrating job description insights into screening logic
- Handling non-traditional career paths
- Reducing false negatives in early screening
- Setting up dual-review workflows
- Calibrating AI models with human feedback loops
Module 5: Ethical AI & Bias Mitigation - Understanding algorithmic bias in recruitment AI
- Direct vs indirect discrimination in automated systems
- Bias audit frameworks for AI hiring tools
- Pre-processing, in-processing, and post-processing bias correction
- Demographic parity and equal opportunity metrics
- Conducting fairness assessments on screening models
- Legal compliance: GDPR, EEOC, and AI regulations
- Documentation requirements for audit trails
- Transparency reporting for AI decisions
- Internal governance models for AI ethics
Module 6: Candidate Assessment & Predictive Analytics - Predictive hiring models: what they can and cannot do
- Using historical hire data to train AI models
- Defining high-performance indicators for AI training
- Assessment scoring consistency across roles
- AI analysis of video interviews and speech patterns
- Text-based assessment analytics for written responses
- Predicting retention risk using behavioural signals
- Adaptive assessment paths based on candidate responses
- Integrating psychometric data with AI scoring
- Setting confidence thresholds for AI predictions
Module 7: AI in Interviewing & Evaluation - Scheduling automation with intelligent calendar matching
- Preliminary AI-led screening interviews
- Real-time interviewer guidance using AI insights
- Note summarisation and key theme extraction
- Consistency scoring across interview panels
- Reducing halo and horn effects with AI feedback
- Benchmarking candidate responses against role profiles
- AI-assisted panel debrief frameworks
- Generating structured evaluation summaries
- Tracking interviewer calibration over time
Module 8: Candidate Experience & Engagement - Measuring candidate sentiment with AI sentiment analysis
- Automated feedback delivery systems
- Chatbots for 24/7 application support
- Personalised communication journeys
- Real-time status updates using AI tracking
- Exit surveys and drop-off analysis
- Improving offer response rates with behavioural nudges
- Onboarding readiness predictions
- Candidate journey mapping with AI insights
- Brand perception analysis from application feedback
Module 9: Data Strategy & AI Tool Selection - Assessing your data readiness for AI
- Data governance principles for recruitment AI
- Integrating HRIS, ATS, and CRM systems
- Evaluating vendor AI capabilities
- The 10-point AI tool scoring rubric
- Comparing off-the-shelf vs custom AI solutions
- API integration requirements with existing platforms
- Data ownership and portability rights
- Security protocols for candidate data
- Deployment models: cloud, hybrid, on-premise
Module 10: Building Your AI Talent Strategy - Conducting a talent AI maturity assessment
- Gap analysis between current and desired state
- Defining your AI implementation scope
- Creating a 90-day action plan
- Identifying quick wins and long-term plays
- Resource allocation and team roles
- Vendor negotiation playbook
- Workforce impact analysis
- Change communication roadmap
- Executive presentation toolkit
Module 11: AI Implementation & Workflow Design - Mapping existing processes for AI integration
- Redesigning workflows with AI handoffs
- Defining human-in-the-loop decision points
- Setting escalation triggers for AI uncertainty
- Version control for AI decision logic
- Feedback integration mechanisms
- Testing AI models with historical data
- Pilot group selection and tracking
- Runbook development for AI operations
- Monitoring AI performance drift
Module 12: Measuring Success & Continuous Improvement - Defining success metrics for AI initiatives
- Dashboard design for AI recruitment KPIs
- Time-to-hire, cost-per-hire, quality-of-hire tracking
- AI recommendation accuracy reporting
- Continuous model retraining schedules
- Root cause analysis for AI errors
- Monthly AI audit checklists
- Feedback collection from hiring managers
- Quarterly strategy refinement cycles
- Scaling successful pilots across departments
Module 13: Advanced AI Applications in Talent - Predictive workforce planning using market data
- Skills gap forecasting with AI trend analysis
- Internal mobility recommendations via AI
- Succession planning powered by performance data
- AI for contractor and gig worker matching
- Global talent pool analysis in real time
- Language and cultural fit assessment
- AI-driven onboarding personalisation
- Post-hire performance validation loops
- Linking hiring AI to L&D pathways
Module 14: Legal, Compliance & Risk Management - Global AI legislation landscape overview
- Compliance with EU AI Act requirements
- US state-level AI disclosure laws
- Consent frameworks for data processing
- Right to explanation and human review
- Recordkeeping obligations for AI decisions
- Risk classification of AI hiring systems
- Third-party vendor compliance audits
- Incident response planning for AI failures
- Insurance and liability considerations
Module 15: Certification & Next Steps - Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption
- Understanding the shift from traditional to AI-enhanced hiring
- Core definitions: machine learning, NLP, predictive analytics in recruitment
- Common misconceptions and myths about AI in HR
- AI maturity models for talent teams
- The ethical imperative: fairness, transparency, and accountability
- Key roles in AI-powered talent acquisition
- How AI impacts candidate experience
- Case study: AI transformation at a global logistics firm
- Mapping AI capabilities to business outcomes
- Key risks and mitigation strategies
Module 2: Strategic Frameworks for AI Integration - The 5-phase AI adoption roadmap
- Building executive alignment and securing stakeholder buy-in
- Developing a talent AI vision statement
- Aligning AI initiatives with DEI goals
- Creating a pilot program charter
- Setting measurable KPIs for AI recruitment success
- Designing a phased rollout plan
- Stakeholder communication templates
- Change management principles for HR teams
- ROI assessment models for AI tools
Module 3: AI-Powered Sourcing & Candidate Discovery - Automated candidate sourcing using AI search logic
- Beyond Boolean: semantic search and contextual matching
- Profile enrichment using AI-driven public data analysis
- Passive candidate identification frameworks
- AI tools for diversity sourcing
- Reducing geographic and gender bias in sourcing
- Building candidate persona clusters
- Automated outreach sequencing principles
- Personalisation at scale using dynamic content
- Tracking sourcing pipeline health with AI
Module 4: Intelligent Screening & Shortlisting - Automated resume parsing accuracy benchmarks
- Extracting experience, skills, and competencies
- Matching algorithms vs human judgment
- Configuring relevance thresholds for screening
- Customising AI filters by role complexity
- Integrating job description insights into screening logic
- Handling non-traditional career paths
- Reducing false negatives in early screening
- Setting up dual-review workflows
- Calibrating AI models with human feedback loops
Module 5: Ethical AI & Bias Mitigation - Understanding algorithmic bias in recruitment AI
- Direct vs indirect discrimination in automated systems
- Bias audit frameworks for AI hiring tools
- Pre-processing, in-processing, and post-processing bias correction
- Demographic parity and equal opportunity metrics
- Conducting fairness assessments on screening models
- Legal compliance: GDPR, EEOC, and AI regulations
- Documentation requirements for audit trails
- Transparency reporting for AI decisions
- Internal governance models for AI ethics
Module 6: Candidate Assessment & Predictive Analytics - Predictive hiring models: what they can and cannot do
- Using historical hire data to train AI models
- Defining high-performance indicators for AI training
- Assessment scoring consistency across roles
- AI analysis of video interviews and speech patterns
- Text-based assessment analytics for written responses
- Predicting retention risk using behavioural signals
- Adaptive assessment paths based on candidate responses
- Integrating psychometric data with AI scoring
- Setting confidence thresholds for AI predictions
Module 7: AI in Interviewing & Evaluation - Scheduling automation with intelligent calendar matching
- Preliminary AI-led screening interviews
- Real-time interviewer guidance using AI insights
- Note summarisation and key theme extraction
- Consistency scoring across interview panels
- Reducing halo and horn effects with AI feedback
- Benchmarking candidate responses against role profiles
- AI-assisted panel debrief frameworks
- Generating structured evaluation summaries
- Tracking interviewer calibration over time
Module 8: Candidate Experience & Engagement - Measuring candidate sentiment with AI sentiment analysis
- Automated feedback delivery systems
- Chatbots for 24/7 application support
- Personalised communication journeys
- Real-time status updates using AI tracking
- Exit surveys and drop-off analysis
- Improving offer response rates with behavioural nudges
- Onboarding readiness predictions
- Candidate journey mapping with AI insights
- Brand perception analysis from application feedback
Module 9: Data Strategy & AI Tool Selection - Assessing your data readiness for AI
- Data governance principles for recruitment AI
- Integrating HRIS, ATS, and CRM systems
- Evaluating vendor AI capabilities
- The 10-point AI tool scoring rubric
- Comparing off-the-shelf vs custom AI solutions
- API integration requirements with existing platforms
- Data ownership and portability rights
- Security protocols for candidate data
- Deployment models: cloud, hybrid, on-premise
Module 10: Building Your AI Talent Strategy - Conducting a talent AI maturity assessment
- Gap analysis between current and desired state
- Defining your AI implementation scope
- Creating a 90-day action plan
- Identifying quick wins and long-term plays
- Resource allocation and team roles
- Vendor negotiation playbook
- Workforce impact analysis
- Change communication roadmap
- Executive presentation toolkit
Module 11: AI Implementation & Workflow Design - Mapping existing processes for AI integration
- Redesigning workflows with AI handoffs
- Defining human-in-the-loop decision points
- Setting escalation triggers for AI uncertainty
- Version control for AI decision logic
- Feedback integration mechanisms
- Testing AI models with historical data
- Pilot group selection and tracking
- Runbook development for AI operations
- Monitoring AI performance drift
Module 12: Measuring Success & Continuous Improvement - Defining success metrics for AI initiatives
- Dashboard design for AI recruitment KPIs
- Time-to-hire, cost-per-hire, quality-of-hire tracking
- AI recommendation accuracy reporting
- Continuous model retraining schedules
- Root cause analysis for AI errors
- Monthly AI audit checklists
- Feedback collection from hiring managers
- Quarterly strategy refinement cycles
- Scaling successful pilots across departments
Module 13: Advanced AI Applications in Talent - Predictive workforce planning using market data
- Skills gap forecasting with AI trend analysis
- Internal mobility recommendations via AI
- Succession planning powered by performance data
- AI for contractor and gig worker matching
- Global talent pool analysis in real time
- Language and cultural fit assessment
- AI-driven onboarding personalisation
- Post-hire performance validation loops
- Linking hiring AI to L&D pathways
Module 14: Legal, Compliance & Risk Management - Global AI legislation landscape overview
- Compliance with EU AI Act requirements
- US state-level AI disclosure laws
- Consent frameworks for data processing
- Right to explanation and human review
- Recordkeeping obligations for AI decisions
- Risk classification of AI hiring systems
- Third-party vendor compliance audits
- Incident response planning for AI failures
- Insurance and liability considerations
Module 15: Certification & Next Steps - Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption
- Automated candidate sourcing using AI search logic
- Beyond Boolean: semantic search and contextual matching
- Profile enrichment using AI-driven public data analysis
- Passive candidate identification frameworks
- AI tools for diversity sourcing
- Reducing geographic and gender bias in sourcing
- Building candidate persona clusters
- Automated outreach sequencing principles
- Personalisation at scale using dynamic content
- Tracking sourcing pipeline health with AI
Module 4: Intelligent Screening & Shortlisting - Automated resume parsing accuracy benchmarks
- Extracting experience, skills, and competencies
- Matching algorithms vs human judgment
- Configuring relevance thresholds for screening
- Customising AI filters by role complexity
- Integrating job description insights into screening logic
- Handling non-traditional career paths
- Reducing false negatives in early screening
- Setting up dual-review workflows
- Calibrating AI models with human feedback loops
Module 5: Ethical AI & Bias Mitigation - Understanding algorithmic bias in recruitment AI
- Direct vs indirect discrimination in automated systems
- Bias audit frameworks for AI hiring tools
- Pre-processing, in-processing, and post-processing bias correction
- Demographic parity and equal opportunity metrics
- Conducting fairness assessments on screening models
- Legal compliance: GDPR, EEOC, and AI regulations
- Documentation requirements for audit trails
- Transparency reporting for AI decisions
- Internal governance models for AI ethics
Module 6: Candidate Assessment & Predictive Analytics - Predictive hiring models: what they can and cannot do
- Using historical hire data to train AI models
- Defining high-performance indicators for AI training
- Assessment scoring consistency across roles
- AI analysis of video interviews and speech patterns
- Text-based assessment analytics for written responses
- Predicting retention risk using behavioural signals
- Adaptive assessment paths based on candidate responses
- Integrating psychometric data with AI scoring
- Setting confidence thresholds for AI predictions
Module 7: AI in Interviewing & Evaluation - Scheduling automation with intelligent calendar matching
- Preliminary AI-led screening interviews
- Real-time interviewer guidance using AI insights
- Note summarisation and key theme extraction
- Consistency scoring across interview panels
- Reducing halo and horn effects with AI feedback
- Benchmarking candidate responses against role profiles
- AI-assisted panel debrief frameworks
- Generating structured evaluation summaries
- Tracking interviewer calibration over time
Module 8: Candidate Experience & Engagement - Measuring candidate sentiment with AI sentiment analysis
- Automated feedback delivery systems
- Chatbots for 24/7 application support
- Personalised communication journeys
- Real-time status updates using AI tracking
- Exit surveys and drop-off analysis
- Improving offer response rates with behavioural nudges
- Onboarding readiness predictions
- Candidate journey mapping with AI insights
- Brand perception analysis from application feedback
Module 9: Data Strategy & AI Tool Selection - Assessing your data readiness for AI
- Data governance principles for recruitment AI
- Integrating HRIS, ATS, and CRM systems
- Evaluating vendor AI capabilities
- The 10-point AI tool scoring rubric
- Comparing off-the-shelf vs custom AI solutions
- API integration requirements with existing platforms
- Data ownership and portability rights
- Security protocols for candidate data
- Deployment models: cloud, hybrid, on-premise
Module 10: Building Your AI Talent Strategy - Conducting a talent AI maturity assessment
- Gap analysis between current and desired state
- Defining your AI implementation scope
- Creating a 90-day action plan
- Identifying quick wins and long-term plays
- Resource allocation and team roles
- Vendor negotiation playbook
- Workforce impact analysis
- Change communication roadmap
- Executive presentation toolkit
Module 11: AI Implementation & Workflow Design - Mapping existing processes for AI integration
- Redesigning workflows with AI handoffs
- Defining human-in-the-loop decision points
- Setting escalation triggers for AI uncertainty
- Version control for AI decision logic
- Feedback integration mechanisms
- Testing AI models with historical data
- Pilot group selection and tracking
- Runbook development for AI operations
- Monitoring AI performance drift
Module 12: Measuring Success & Continuous Improvement - Defining success metrics for AI initiatives
- Dashboard design for AI recruitment KPIs
- Time-to-hire, cost-per-hire, quality-of-hire tracking
- AI recommendation accuracy reporting
- Continuous model retraining schedules
- Root cause analysis for AI errors
- Monthly AI audit checklists
- Feedback collection from hiring managers
- Quarterly strategy refinement cycles
- Scaling successful pilots across departments
Module 13: Advanced AI Applications in Talent - Predictive workforce planning using market data
- Skills gap forecasting with AI trend analysis
- Internal mobility recommendations via AI
- Succession planning powered by performance data
- AI for contractor and gig worker matching
- Global talent pool analysis in real time
- Language and cultural fit assessment
- AI-driven onboarding personalisation
- Post-hire performance validation loops
- Linking hiring AI to L&D pathways
Module 14: Legal, Compliance & Risk Management - Global AI legislation landscape overview
- Compliance with EU AI Act requirements
- US state-level AI disclosure laws
- Consent frameworks for data processing
- Right to explanation and human review
- Recordkeeping obligations for AI decisions
- Risk classification of AI hiring systems
- Third-party vendor compliance audits
- Incident response planning for AI failures
- Insurance and liability considerations
Module 15: Certification & Next Steps - Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption
- Understanding algorithmic bias in recruitment AI
- Direct vs indirect discrimination in automated systems
- Bias audit frameworks for AI hiring tools
- Pre-processing, in-processing, and post-processing bias correction
- Demographic parity and equal opportunity metrics
- Conducting fairness assessments on screening models
- Legal compliance: GDPR, EEOC, and AI regulations
- Documentation requirements for audit trails
- Transparency reporting for AI decisions
- Internal governance models for AI ethics
Module 6: Candidate Assessment & Predictive Analytics - Predictive hiring models: what they can and cannot do
- Using historical hire data to train AI models
- Defining high-performance indicators for AI training
- Assessment scoring consistency across roles
- AI analysis of video interviews and speech patterns
- Text-based assessment analytics for written responses
- Predicting retention risk using behavioural signals
- Adaptive assessment paths based on candidate responses
- Integrating psychometric data with AI scoring
- Setting confidence thresholds for AI predictions
Module 7: AI in Interviewing & Evaluation - Scheduling automation with intelligent calendar matching
- Preliminary AI-led screening interviews
- Real-time interviewer guidance using AI insights
- Note summarisation and key theme extraction
- Consistency scoring across interview panels
- Reducing halo and horn effects with AI feedback
- Benchmarking candidate responses against role profiles
- AI-assisted panel debrief frameworks
- Generating structured evaluation summaries
- Tracking interviewer calibration over time
Module 8: Candidate Experience & Engagement - Measuring candidate sentiment with AI sentiment analysis
- Automated feedback delivery systems
- Chatbots for 24/7 application support
- Personalised communication journeys
- Real-time status updates using AI tracking
- Exit surveys and drop-off analysis
- Improving offer response rates with behavioural nudges
- Onboarding readiness predictions
- Candidate journey mapping with AI insights
- Brand perception analysis from application feedback
Module 9: Data Strategy & AI Tool Selection - Assessing your data readiness for AI
- Data governance principles for recruitment AI
- Integrating HRIS, ATS, and CRM systems
- Evaluating vendor AI capabilities
- The 10-point AI tool scoring rubric
- Comparing off-the-shelf vs custom AI solutions
- API integration requirements with existing platforms
- Data ownership and portability rights
- Security protocols for candidate data
- Deployment models: cloud, hybrid, on-premise
Module 10: Building Your AI Talent Strategy - Conducting a talent AI maturity assessment
- Gap analysis between current and desired state
- Defining your AI implementation scope
- Creating a 90-day action plan
- Identifying quick wins and long-term plays
- Resource allocation and team roles
- Vendor negotiation playbook
- Workforce impact analysis
- Change communication roadmap
- Executive presentation toolkit
Module 11: AI Implementation & Workflow Design - Mapping existing processes for AI integration
- Redesigning workflows with AI handoffs
- Defining human-in-the-loop decision points
- Setting escalation triggers for AI uncertainty
- Version control for AI decision logic
- Feedback integration mechanisms
- Testing AI models with historical data
- Pilot group selection and tracking
- Runbook development for AI operations
- Monitoring AI performance drift
Module 12: Measuring Success & Continuous Improvement - Defining success metrics for AI initiatives
- Dashboard design for AI recruitment KPIs
- Time-to-hire, cost-per-hire, quality-of-hire tracking
- AI recommendation accuracy reporting
- Continuous model retraining schedules
- Root cause analysis for AI errors
- Monthly AI audit checklists
- Feedback collection from hiring managers
- Quarterly strategy refinement cycles
- Scaling successful pilots across departments
Module 13: Advanced AI Applications in Talent - Predictive workforce planning using market data
- Skills gap forecasting with AI trend analysis
- Internal mobility recommendations via AI
- Succession planning powered by performance data
- AI for contractor and gig worker matching
- Global talent pool analysis in real time
- Language and cultural fit assessment
- AI-driven onboarding personalisation
- Post-hire performance validation loops
- Linking hiring AI to L&D pathways
Module 14: Legal, Compliance & Risk Management - Global AI legislation landscape overview
- Compliance with EU AI Act requirements
- US state-level AI disclosure laws
- Consent frameworks for data processing
- Right to explanation and human review
- Recordkeeping obligations for AI decisions
- Risk classification of AI hiring systems
- Third-party vendor compliance audits
- Incident response planning for AI failures
- Insurance and liability considerations
Module 15: Certification & Next Steps - Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption
- Scheduling automation with intelligent calendar matching
- Preliminary AI-led screening interviews
- Real-time interviewer guidance using AI insights
- Note summarisation and key theme extraction
- Consistency scoring across interview panels
- Reducing halo and horn effects with AI feedback
- Benchmarking candidate responses against role profiles
- AI-assisted panel debrief frameworks
- Generating structured evaluation summaries
- Tracking interviewer calibration over time
Module 8: Candidate Experience & Engagement - Measuring candidate sentiment with AI sentiment analysis
- Automated feedback delivery systems
- Chatbots for 24/7 application support
- Personalised communication journeys
- Real-time status updates using AI tracking
- Exit surveys and drop-off analysis
- Improving offer response rates with behavioural nudges
- Onboarding readiness predictions
- Candidate journey mapping with AI insights
- Brand perception analysis from application feedback
Module 9: Data Strategy & AI Tool Selection - Assessing your data readiness for AI
- Data governance principles for recruitment AI
- Integrating HRIS, ATS, and CRM systems
- Evaluating vendor AI capabilities
- The 10-point AI tool scoring rubric
- Comparing off-the-shelf vs custom AI solutions
- API integration requirements with existing platforms
- Data ownership and portability rights
- Security protocols for candidate data
- Deployment models: cloud, hybrid, on-premise
Module 10: Building Your AI Talent Strategy - Conducting a talent AI maturity assessment
- Gap analysis between current and desired state
- Defining your AI implementation scope
- Creating a 90-day action plan
- Identifying quick wins and long-term plays
- Resource allocation and team roles
- Vendor negotiation playbook
- Workforce impact analysis
- Change communication roadmap
- Executive presentation toolkit
Module 11: AI Implementation & Workflow Design - Mapping existing processes for AI integration
- Redesigning workflows with AI handoffs
- Defining human-in-the-loop decision points
- Setting escalation triggers for AI uncertainty
- Version control for AI decision logic
- Feedback integration mechanisms
- Testing AI models with historical data
- Pilot group selection and tracking
- Runbook development for AI operations
- Monitoring AI performance drift
Module 12: Measuring Success & Continuous Improvement - Defining success metrics for AI initiatives
- Dashboard design for AI recruitment KPIs
- Time-to-hire, cost-per-hire, quality-of-hire tracking
- AI recommendation accuracy reporting
- Continuous model retraining schedules
- Root cause analysis for AI errors
- Monthly AI audit checklists
- Feedback collection from hiring managers
- Quarterly strategy refinement cycles
- Scaling successful pilots across departments
Module 13: Advanced AI Applications in Talent - Predictive workforce planning using market data
- Skills gap forecasting with AI trend analysis
- Internal mobility recommendations via AI
- Succession planning powered by performance data
- AI for contractor and gig worker matching
- Global talent pool analysis in real time
- Language and cultural fit assessment
- AI-driven onboarding personalisation
- Post-hire performance validation loops
- Linking hiring AI to L&D pathways
Module 14: Legal, Compliance & Risk Management - Global AI legislation landscape overview
- Compliance with EU AI Act requirements
- US state-level AI disclosure laws
- Consent frameworks for data processing
- Right to explanation and human review
- Recordkeeping obligations for AI decisions
- Risk classification of AI hiring systems
- Third-party vendor compliance audits
- Incident response planning for AI failures
- Insurance and liability considerations
Module 15: Certification & Next Steps - Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption
- Assessing your data readiness for AI
- Data governance principles for recruitment AI
- Integrating HRIS, ATS, and CRM systems
- Evaluating vendor AI capabilities
- The 10-point AI tool scoring rubric
- Comparing off-the-shelf vs custom AI solutions
- API integration requirements with existing platforms
- Data ownership and portability rights
- Security protocols for candidate data
- Deployment models: cloud, hybrid, on-premise
Module 10: Building Your AI Talent Strategy - Conducting a talent AI maturity assessment
- Gap analysis between current and desired state
- Defining your AI implementation scope
- Creating a 90-day action plan
- Identifying quick wins and long-term plays
- Resource allocation and team roles
- Vendor negotiation playbook
- Workforce impact analysis
- Change communication roadmap
- Executive presentation toolkit
Module 11: AI Implementation & Workflow Design - Mapping existing processes for AI integration
- Redesigning workflows with AI handoffs
- Defining human-in-the-loop decision points
- Setting escalation triggers for AI uncertainty
- Version control for AI decision logic
- Feedback integration mechanisms
- Testing AI models with historical data
- Pilot group selection and tracking
- Runbook development for AI operations
- Monitoring AI performance drift
Module 12: Measuring Success & Continuous Improvement - Defining success metrics for AI initiatives
- Dashboard design for AI recruitment KPIs
- Time-to-hire, cost-per-hire, quality-of-hire tracking
- AI recommendation accuracy reporting
- Continuous model retraining schedules
- Root cause analysis for AI errors
- Monthly AI audit checklists
- Feedback collection from hiring managers
- Quarterly strategy refinement cycles
- Scaling successful pilots across departments
Module 13: Advanced AI Applications in Talent - Predictive workforce planning using market data
- Skills gap forecasting with AI trend analysis
- Internal mobility recommendations via AI
- Succession planning powered by performance data
- AI for contractor and gig worker matching
- Global talent pool analysis in real time
- Language and cultural fit assessment
- AI-driven onboarding personalisation
- Post-hire performance validation loops
- Linking hiring AI to L&D pathways
Module 14: Legal, Compliance & Risk Management - Global AI legislation landscape overview
- Compliance with EU AI Act requirements
- US state-level AI disclosure laws
- Consent frameworks for data processing
- Right to explanation and human review
- Recordkeeping obligations for AI decisions
- Risk classification of AI hiring systems
- Third-party vendor compliance audits
- Incident response planning for AI failures
- Insurance and liability considerations
Module 15: Certification & Next Steps - Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption
- Mapping existing processes for AI integration
- Redesigning workflows with AI handoffs
- Defining human-in-the-loop decision points
- Setting escalation triggers for AI uncertainty
- Version control for AI decision logic
- Feedback integration mechanisms
- Testing AI models with historical data
- Pilot group selection and tracking
- Runbook development for AI operations
- Monitoring AI performance drift
Module 12: Measuring Success & Continuous Improvement - Defining success metrics for AI initiatives
- Dashboard design for AI recruitment KPIs
- Time-to-hire, cost-per-hire, quality-of-hire tracking
- AI recommendation accuracy reporting
- Continuous model retraining schedules
- Root cause analysis for AI errors
- Monthly AI audit checklists
- Feedback collection from hiring managers
- Quarterly strategy refinement cycles
- Scaling successful pilots across departments
Module 13: Advanced AI Applications in Talent - Predictive workforce planning using market data
- Skills gap forecasting with AI trend analysis
- Internal mobility recommendations via AI
- Succession planning powered by performance data
- AI for contractor and gig worker matching
- Global talent pool analysis in real time
- Language and cultural fit assessment
- AI-driven onboarding personalisation
- Post-hire performance validation loops
- Linking hiring AI to L&D pathways
Module 14: Legal, Compliance & Risk Management - Global AI legislation landscape overview
- Compliance with EU AI Act requirements
- US state-level AI disclosure laws
- Consent frameworks for data processing
- Right to explanation and human review
- Recordkeeping obligations for AI decisions
- Risk classification of AI hiring systems
- Third-party vendor compliance audits
- Incident response planning for AI failures
- Insurance and liability considerations
Module 15: Certification & Next Steps - Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption
- Predictive workforce planning using market data
- Skills gap forecasting with AI trend analysis
- Internal mobility recommendations via AI
- Succession planning powered by performance data
- AI for contractor and gig worker matching
- Global talent pool analysis in real time
- Language and cultural fit assessment
- AI-driven onboarding personalisation
- Post-hire performance validation loops
- Linking hiring AI to L&D pathways
Module 14: Legal, Compliance & Risk Management - Global AI legislation landscape overview
- Compliance with EU AI Act requirements
- US state-level AI disclosure laws
- Consent frameworks for data processing
- Right to explanation and human review
- Recordkeeping obligations for AI decisions
- Risk classification of AI hiring systems
- Third-party vendor compliance audits
- Incident response planning for AI failures
- Insurance and liability considerations
Module 15: Certification & Next Steps - Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption
- Final project: develop your AI talent acquisition strategy
- Strategy template and grading rubric
- Submission process for Certificate of Completion
- Feedback and revision process
- How to showcase your certification professionally
- LinkedIn optimisation for AI competency signals
- Continuing education pathways
- Community access and peer networking
- Refresher content update notifications
- Transitioning from learner to leader in AI adoption