Module 1: Strategic HR in the AI-Enabled Organization
Focus: Positioning HR as the architect of workforce transformation, not only as a user of AI tools.
- AI, automation and augmentation across the employee lifecycle.
- Strategic HR priorities: capability, capacity, productivity, experience and risk.
- How AI changes workforce structures, decision rights and managerial responsibilities.
- The roles of HR, business leaders, IT, data, legal, risk and employees in transformation.
Module 2: AI Readiness Assessment for HR and the Workforce
Focus: Establishing whether the organization, HR function and workforce are prepared to adopt AI responsibly.
- Readiness dimensions: strategy, leadership, people, process, data, technology and governance.
- HR-function readiness versus enterprise and employee readiness.
- AI literacy, trust, willingness to adopt and manager readiness indicators.
- Maturity scoring, readiness heat maps, gaps and prioritized interventions.
Module 3: HR Data and Evidence Foundation
Focus: Building reliable workforce evidence before redesigning jobs or measuring AI impact.
- Required HR data: jobs, skills, workload, performance, learning, mobility and employee experience.
- Data quality, ownership, access, privacy, consent and retention considerations.
- Bias risks in historical HR data and the limits of AI-generated recommendations.
- Creating a baseline for current workforce capacity, cost, quality and performance.
Module 4: Job Analysis and Task Decomposition
Focus: Understanding the real work performed before deciding where AI should be introduced.
- Mapping current roles, workflows, recurring tasks, decisions and handoffs.
- Separating a job from its component tasks, skills, knowledge and accountabilities.
- Identifying repetitive work, bottlenecks, judgment-intensive work and human-critical activities.
- Documenting time, frequency, complexity, risk and value for each task.
Module 5: Job Reengineering and Human-AI Work Allocation
Focus: Redesigning work around the strengths and limits of people and AI.
- Classifying tasks as automate, augment, redesign, eliminate or retain as human-led.
- Defining human checkpoints, exception handling, escalation and accountability.
- Redesigning workflows, role boundaries, decision rights and spans of responsibility.
- Updating job purpose, duties, outputs, competencies and required AI interaction in job descriptions.
Module 6: Strategic Workforce Planning and Role Transition
Focus: Translating job-level change into workforce capacity and talent decisions.
- Assessing AI impact by job family, grade, function and workforce segment.
- Modeling capacity, demand, redeployment and future workforce scenarios.
- Identifying emerging roles and changed responsibilities in AI-enabled teams.
- Planning fair role transitions, internal mobility and succession implications.
Module 7: AI Competency and Future Skills Framework
Focus: Defining what employees and managers must know and do in redesigned roles.
- AI literacy, functional AI proficiency, data judgment and responsible-use competencies.
- Human capabilities that gain importance: critical thinking, creativity, empathy and accountability.
- Competency levels by role and the evidence required to demonstrate proficiency.
- Skills inventory, gap analysis and prioritization by strategic value and urgency.
Module 8: Reskilling, Upskilling and Change Adoption
Focus: Preparing employees to perform successfully in redesigned roles.
- Role-based learning pathways for employees, managers, HR teams and AI champions.
- Reskilling, upskilling, redeployment and selective external hiring decisions.
- Addressing resistance, fear, trust, workload pressure and change fatigue.
- Measuring learning transfer, adoption, confidence and on-the-job application.
Module 9: AI-Enabled Performance Management
Focus: Moving from periodic appraisal toward continuous, evidence-based performance enablement.
- Using AI to support goal setting, feedback, coaching, development and performance insights.
- Combining quantitative evidence with manager judgment and employee context.
- Separating legitimate performance measurement from intrusive employee surveillance.
- Keeping promotions, pay, disciplinary action and termination under accountable human review.
Module 10: Redesigning KPIs for Human-AI Performance
Focus: Measuring value created through the redesigned role rather than activity volume alone.
- Aligning role-level measures with strategic business and workforce objectives.
- Balancing productivity, quality, speed, customer outcomes, innovation and employee experience.
- Distinguishing AI output, employee contribution and combined human-AI performance.
- Setting targets, thresholds and safeguards that do not reward unsafe or biased behavior.
Module 11: Before-and-After AI Performance Evaluation
Focus: Determining whether AI and job redesign produced measurable, sustainable improvement.
- Defining pre-AI baselines and post-implementation measurement periods.
- Using pilots, comparison groups, trend analysis and qualitative employee feedback.
- Measuring adoption, time saved, output quality, error rates, cost, service and business results.
- Evaluating ROI, workforce impact, unintended consequences and corrective actions.
Module 12: Responsible AI Governance and HR Transformation Roadmap
Focus: Converting the course frameworks into a controlled and actionable implementation plan.
- Governance roles, approval authorities, model monitoring and documentation requirements.
- Fairness, explainability, privacy, security, accessibility and employee appeal mechanisms.
- Prioritizing use cases by value, readiness, workforce impact and risk.
- Building a phased roadmap covering assessment, pilot, job redesign, capability building, evaluation and scale-up.
