The AI Transformation of HR — and Why Every HR Professional Must Adapt Now
Human Resources has always been fundamentally about people — finding the right people, developing them, keeping them engaged and productive, and building cultures where they can contribute their best work. None of that changes with AI. What changes is the capacity, speed, and precision with which HR professionals can do their work. The HR team that previously spent three weeks shortlisting 2,000 applications can now do it in two hours. The CHRO who previously had to wait for quarterly survey results to know whether employee engagement was declining can now see sentiment signals in real-time. The workforce planner who previously built headcount models in Excel over three weeks can now run fifty scenarios in an afternoon using predictive analytics platforms.
🎓 Next Batch Starting Soon — Limited Seats
Free demo class available • EMI facility available • 100% placement support
The organisations that are winning the talent war right now are those whose HR functions have embraced AI — and the HR professionals who are commanding the highest salaries and advancing the fastest are those who can operate at the intersection of people expertise and AI capability. Companies like Infosys, TCS, Wipro, Bajaj Finserv, and Mahindra across Pune are actively building AI-augmented HR functions and urgently need HR professionals who speak both languages: the language of people and the language of AI.
The Aapvex AI HR Professional Programme is the bridge. It does not require you to learn programming or become a data scientist. It requires you to understand what AI can do in HR, how to evaluate AI HR tools critically, how to use the analytics and automation capabilities in modern HRIS platforms, how to leverage Generative AI for HR content and workflows, and how to lead the ethical deployment of AI in HR processes. These are skills that immediately differentiate you in job applications and salary negotiations. Call 7796731656 to speak to an advisor today.
500+
Students Placed
4.9★
Average Rating
8
Course Modules
40–80%
Salary Premium for AI HR Skills
The Business Impact of AI in HR — Numbers That Matter
75%
Reduction in resume shortlisting time with AI screening tools
60%
Of routine HR queries handled by AI chatbots without human intervention
85%
Accuracy of AI attrition prediction models on historical employee data
3x
Increase in recruiter productivity using AI-powered talent intelligence platforms
40%
Reduction in time-to-hire at organisations using AI in recruitment workflows
₹18L+
Average salary for People Analytics Managers in Pune's top organisations
AI HR Tools You Will Work With
ChatGPT / Claude
HR content & workflows
Darwinbox AI
AI-powered HRIS
Keka AI
HR automation platform
LinkedIn Talent Insights
Talent market analytics
HireVue
AI video interviewing
Eightfold AI
Talent intelligence
Pymetrics
Neuro-AI assessment
Power BI
HR dashboards & reports
SAP SuccessFactors AI
Enterprise HR AI
Workday AI
Workforce planning AI
SeekOut / Entelo
AI candidate sourcing
Python (No-Code)
Basic HR data analysis
Detailed Curriculum — 8 Comprehensive Modules
This programme is structured for HR professionals — it begins with the AI concepts every HR leader must understand, builds through hands-on work with real AI HR tools, and culminates in practical AI project work that you can immediately apply in your current or next role.
1
AI Fundamentals for HR Professionals — What AI Is, What It Can Do, and What It Cannot
Before applying AI to HR, you need a solid conceptual foundation — not programming skills, but genuine understanding of how AI systems work, why they fail, and what questions HR professionals must ask when evaluating any AI HR product. This module is the AI literacy foundation that makes everything else in the programme coherent.
AI, Machine Learning, and Deep Learning are demystified — not with mathematics, but with clear mental models and HR-specific examples. You will understand: how a resume screening AI actually works (it learns patterns from historical hiring decisions — which means it can learn the wrong patterns just as easily as the right ones). How a predictive attrition model works (it identifies statistical correlations in employee data — not causes). Why an AI performance rating system can be simultaneously statistically accurate and fundamentally unfair. The major categories of AI used in HR — Natural Language Processing for text analysis, predictive analytics for forecasting, computer vision for video interviews, recommendation systems for internal mobility — are each introduced with real-world examples and evaluated for genuine business value versus marketing hype. The current AI HR technology landscape is mapped: from HRIS platforms with embedded AI features (Darwinbox, Keka, Workday, SuccessFactors) to specialised AI recruitment tools (Eightfold, HireVue, Pymetrics) to analytics platforms (Visier, OrgVantage) — you will understand the landscape well enough to evaluate vendors, ask the right questions in demos, and make informed technology investment recommendations.
AI, Machine Learning, and Deep Learning are demystified — not with mathematics, but with clear mental models and HR-specific examples. You will understand: how a resume screening AI actually works (it learns patterns from historical hiring decisions — which means it can learn the wrong patterns just as easily as the right ones). How a predictive attrition model works (it identifies statistical correlations in employee data — not causes). Why an AI performance rating system can be simultaneously statistically accurate and fundamentally unfair. The major categories of AI used in HR — Natural Language Processing for text analysis, predictive analytics for forecasting, computer vision for video interviews, recommendation systems for internal mobility — are each introduced with real-world examples and evaluated for genuine business value versus marketing hype. The current AI HR technology landscape is mapped: from HRIS platforms with embedded AI features (Darwinbox, Keka, Workday, SuccessFactors) to specialised AI recruitment tools (Eightfold, HireVue, Pymetrics) to analytics platforms (Visier, OrgVantage) — you will understand the landscape well enough to evaluate vendors, ask the right questions in demos, and make informed technology investment recommendations.
2
AI in Talent Acquisition — Intelligent Recruiting, Screening & Candidate Experience
Talent acquisition is where AI has made the most visible and commercially impactful inroads in HR — and it is where the risks of AI misuse are also highest. This module covers the full AI-augmented recruitment workflow: from intelligent job description creation through AI-powered sourcing, screening, assessment, and interview scheduling — with critical examination of where each step adds genuine value and where human judgment remains essential.
AI job description creation using Generative AI tools is covered hands-on: using ChatGPT and specialised HR AI tools to write inclusive, structured, competency-mapped job descriptions — and using bias detection tools to audit the output for gendered language, socioeconomic exclusion signals, and unnecessarily restrictive requirements. AI-powered candidate sourcing tools are evaluated in depth: LinkedIn Talent Insights for talent pool mapping and competitive salary benchmarking, SeekOut and Entelo for diversity-focused candidate discovery, and Eightfold AI for skills-based talent matching that identifies qualified candidates that keyword-based ATS systems miss. Resume screening AI — how it works, how to configure it, how to audit its decisions for bias, and the critical compliance questions around automated screening decisions — is covered as a practitioner's workflow. AI video interviews using HireVue are demonstrated and critically evaluated: the technology, the assessment methodology, the validity evidence, the candidate experience implications, and the legal and ethical considerations for Indian HR contexts. AI-powered psychometric assessments (Pymetrics, Criteria, Arctic Shores) are evaluated with a focus on predictive validity, test-retest reliability, and adverse impact analysis. The module includes a complete AI recruitment workflow simulation — sourcing, screening, and shortlisting for a real job opening using the tools covered.
AI job description creation using Generative AI tools is covered hands-on: using ChatGPT and specialised HR AI tools to write inclusive, structured, competency-mapped job descriptions — and using bias detection tools to audit the output for gendered language, socioeconomic exclusion signals, and unnecessarily restrictive requirements. AI-powered candidate sourcing tools are evaluated in depth: LinkedIn Talent Insights for talent pool mapping and competitive salary benchmarking, SeekOut and Entelo for diversity-focused candidate discovery, and Eightfold AI for skills-based talent matching that identifies qualified candidates that keyword-based ATS systems miss. Resume screening AI — how it works, how to configure it, how to audit its decisions for bias, and the critical compliance questions around automated screening decisions — is covered as a practitioner's workflow. AI video interviews using HireVue are demonstrated and critically evaluated: the technology, the assessment methodology, the validity evidence, the candidate experience implications, and the legal and ethical considerations for Indian HR contexts. AI-powered psychometric assessments (Pymetrics, Criteria, Arctic Shores) are evaluated with a focus on predictive validity, test-retest reliability, and adverse impact analysis. The module includes a complete AI recruitment workflow simulation — sourcing, screening, and shortlisting for a real job opening using the tools covered.
3
HR Analytics & People Data — From Descriptive to Predictive
Data is the foundation of every AI HR system. HR professionals who understand how to work with people data — how to structure it, clean it, visualise it, and derive insights from it — are dramatically more effective at implementing AI HR tools and much better positioned to challenge AI outputs when they seem wrong. This module builds genuine data literacy specifically for HR use cases.
HR data ecosystem mapping is covered first: the data sources available in a typical organisation — HRIS records, ATS data, performance data, compensation data, learning management system data, engagement survey data, exit interview data, payroll data — and the data governance questions that determine what can be used for what purpose. Excel-based HR analytics is covered as the no-code starting point: pivot tables for headcount and attrition reporting, XLOOKUP and advanced formulas for data joining across HR data sources, and chart creation for management reporting. Power BI is introduced as the professional HR analytics tool: data loading from multiple HR system exports, data modelling with relationships, DAX measures for HR KPIs (headcount, attrition rate, time-to-hire, cost-per-hire, offer acceptance rate, training completion), and interactive dashboard creation that allows leaders to filter by department, level, tenure band, and other dimensions. Descriptive analytics dashboards — the foundation of HR reporting — are built hands-on, followed by diagnostic analytics (why did attrition spike in Q3?) and predictive analytics introduction using Excel-based regression and Power BI's built-in forecasting features. A complete People Analytics dashboard covering headcount, movement, attrition, and recruitment metrics is the module deliverable.
HR data ecosystem mapping is covered first: the data sources available in a typical organisation — HRIS records, ATS data, performance data, compensation data, learning management system data, engagement survey data, exit interview data, payroll data — and the data governance questions that determine what can be used for what purpose. Excel-based HR analytics is covered as the no-code starting point: pivot tables for headcount and attrition reporting, XLOOKUP and advanced formulas for data joining across HR data sources, and chart creation for management reporting. Power BI is introduced as the professional HR analytics tool: data loading from multiple HR system exports, data modelling with relationships, DAX measures for HR KPIs (headcount, attrition rate, time-to-hire, cost-per-hire, offer acceptance rate, training completion), and interactive dashboard creation that allows leaders to filter by department, level, tenure band, and other dimensions. Descriptive analytics dashboards — the foundation of HR reporting — are built hands-on, followed by diagnostic analytics (why did attrition spike in Q3?) and predictive analytics introduction using Excel-based regression and Power BI's built-in forecasting features. A complete People Analytics dashboard covering headcount, movement, attrition, and recruitment metrics is the module deliverable.
4
Predictive Workforce Analytics — Attrition, Performance & Workforce Planning AI
Predictive analytics is where HR moves from describing what happened to forecasting what will happen — and it is the capability that delivers the highest ROI from HR data investments. This module covers the most commercially valuable predictive HR analytics use cases: attrition prediction, performance forecasting, flight risk identification, and AI-driven workforce planning.
Attrition prediction modelling is the centrepiece of this module — building a machine learning model using historical employee data that predicts which current employees are most likely to resign in the next 6 months. Using Python in a no-code Jupyter environment with pre-written templates, students load a realistic anonymised employee dataset, perform exploratory analysis to understand attrition patterns, build a Logistic Regression and Random Forest classifier, evaluate model performance using precision-recall trade-offs (in HR contexts, the cost of false positives and false negatives is different — retaining someone who would have stayed costs money, but missing someone who leaves costs a replacement hire), and generate a scored employee list with individual attrition probability and contributing factors using SHAP values. Performance prediction models — using tenure, learning activity, engagement scores, and manager effectiveness ratings to predict performance review outcomes — are built with the same framework. Workforce planning AI is introduced: headcount demand forecasting using business driver-based models (revenue per head, project pipeline volume, seasonal demand patterns), skills gap analysis using current workforce capability data versus strategic skill requirements, and scenario planning (what happens to our headcount if revenue grows 30%? if we automate this process? if we expand to a new geography?). Visier and Workday Prism Analytics are demonstrated as enterprise-grade people analytics platforms.
Attrition prediction modelling is the centrepiece of this module — building a machine learning model using historical employee data that predicts which current employees are most likely to resign in the next 6 months. Using Python in a no-code Jupyter environment with pre-written templates, students load a realistic anonymised employee dataset, perform exploratory analysis to understand attrition patterns, build a Logistic Regression and Random Forest classifier, evaluate model performance using precision-recall trade-offs (in HR contexts, the cost of false positives and false negatives is different — retaining someone who would have stayed costs money, but missing someone who leaves costs a replacement hire), and generate a scored employee list with individual attrition probability and contributing factors using SHAP values. Performance prediction models — using tenure, learning activity, engagement scores, and manager effectiveness ratings to predict performance review outcomes — are built with the same framework. Workforce planning AI is introduced: headcount demand forecasting using business driver-based models (revenue per head, project pipeline volume, seasonal demand patterns), skills gap analysis using current workforce capability data versus strategic skill requirements, and scenario planning (what happens to our headcount if revenue grows 30%? if we automate this process? if we expand to a new geography?). Visier and Workday Prism Analytics are demonstrated as enterprise-grade people analytics platforms.
5
Employee Sentiment Analysis & Engagement AI
Traditional employee engagement measurement — annual surveys, quarterly pulse checks — has a fundamental flaw: it is a lagging indicator. By the time survey results are analysed and reported, the engagement issue has already affected retention, productivity, and team dynamics. AI-powered sentiment analysis provides real-time insight into how employees are feeling — from internal communication patterns, survey text responses, exit interviews, Glassdoor reviews, and other text data sources — enabling proactive rather than reactive people management.
This module covers the theory and practice of sentiment analysis specifically in HR contexts. The NLP technology behind sentiment analysis tools is explained without requiring programming — how models classify text as positive, negative, or neutral, and more sophisticated models that detect specific emotion categories (frustration, enthusiasm, concern, disengagement). Practical hands-on work covers: using ChatGPT to batch-analyse qualitative survey responses and identify themes and sentiment patterns, using Excel-based sentiment scoring tools for structured text analysis, and evaluating commercial employee sentiment platforms (Leena AI, Glint, Peakon, Culture Amp) for their AI capabilities, data requirements, and implementation complexity. Employee Net Promoter Score (eNPS) trend analysis and text mining of verbatim comments is performed hands-on. Exit interview text analysis — using AI to identify systemic departure reasons that interview bias might obscure — is built as a practical HR tool. The module includes an ethics discussion on the boundaries of employee sentiment monitoring: what constitutes legitimate engagement measurement versus invasive surveillance, the consent and transparency requirements, and the legal landscape in India under the Digital Personal Data Protection Act.
This module covers the theory and practice of sentiment analysis specifically in HR contexts. The NLP technology behind sentiment analysis tools is explained without requiring programming — how models classify text as positive, negative, or neutral, and more sophisticated models that detect specific emotion categories (frustration, enthusiasm, concern, disengagement). Practical hands-on work covers: using ChatGPT to batch-analyse qualitative survey responses and identify themes and sentiment patterns, using Excel-based sentiment scoring tools for structured text analysis, and evaluating commercial employee sentiment platforms (Leena AI, Glint, Peakon, Culture Amp) for their AI capabilities, data requirements, and implementation complexity. Employee Net Promoter Score (eNPS) trend analysis and text mining of verbatim comments is performed hands-on. Exit interview text analysis — using AI to identify systemic departure reasons that interview bias might obscure — is built as a practical HR tool. The module includes an ethics discussion on the boundaries of employee sentiment monitoring: what constitutes legitimate engagement measurement versus invasive surveillance, the consent and transparency requirements, and the legal landscape in India under the Digital Personal Data Protection Act.
6
Generative AI for HR — ChatGPT, Copilot & AI-Powered HR Content Creation
Generative AI — the technology behind ChatGPT, Microsoft Copilot, and Claude — is the most immediately usable AI tool for HR professionals who do not have a technical background. It can draft job descriptions, generate interview question banks, write HR policies, summarise employee feedback, create onboarding materials, produce performance review templates, and analyse salary survey data — all in minutes rather than hours. This module makes you a power user of Generative AI for HR workflows.
Prompt engineering for HR is covered as a systematic skill — not intuition, but a structured approach to getting consistently high-quality outputs from AI tools. The RICE framework for HR prompts (Role, Instructions, Context, Examples) is taught and practised across twenty HR use cases. Hands-on exercises cover: writing inclusive, competency-based job descriptions with AI, generating structured behavioural interview question banks aligned to specific competencies, drafting HR policies (code of conduct, remote work policy, performance improvement plan templates) using AI with proper review frameworks, summarising large volumes of employee feedback using AI to identify actionable themes, creating personalised onboarding welcome messages and learning pathways, and building AI-assisted salary benchmarking reports. Microsoft Copilot for HR is demonstrated in Word, Excel, and Outlook — showing how Copilot accelerates the everyday document and data work that consumes significant HR professional time. The limitations and risks of Generative AI in HR are covered with the same rigour as the capabilities: hallucination, confidentiality, copyright, bias amplification, and the quality review processes that must accompany any AI-generated HR content. Students build a library of fifteen HR prompt templates — their personal AI workflow toolkit for immediate use after the programme.
Prompt engineering for HR is covered as a systematic skill — not intuition, but a structured approach to getting consistently high-quality outputs from AI tools. The RICE framework for HR prompts (Role, Instructions, Context, Examples) is taught and practised across twenty HR use cases. Hands-on exercises cover: writing inclusive, competency-based job descriptions with AI, generating structured behavioural interview question banks aligned to specific competencies, drafting HR policies (code of conduct, remote work policy, performance improvement plan templates) using AI with proper review frameworks, summarising large volumes of employee feedback using AI to identify actionable themes, creating personalised onboarding welcome messages and learning pathways, and building AI-assisted salary benchmarking reports. Microsoft Copilot for HR is demonstrated in Word, Excel, and Outlook — showing how Copilot accelerates the everyday document and data work that consumes significant HR professional time. The limitations and risks of Generative AI in HR are covered with the same rigour as the capabilities: hallucination, confidentiality, copyright, bias amplification, and the quality review processes that must accompany any AI-generated HR content. Students build a library of fifteen HR prompt templates — their personal AI workflow toolkit for immediate use after the programme.
7
HR Chatbots, HRIS Automation & AI-Powered Employee Experience
HR chatbots are one of the highest-ROI AI implementations in HR — handling repetitive employee queries (leave balances, policy questions, payslip requests, onboarding information) automatically, 24/7, at zero marginal cost per query. This module covers the design, implementation, and management of HR chatbots and the broader category of HRIS process automation.
The HR chatbot design framework is covered from the ground up: intent mapping (what questions do employees actually ask HR, and in what volumes?), conversation flow design, escalation logic (when should the chatbot hand off to a human?), and integration with HRIS systems for real-time data retrieval. Darwinbox's AI assistant and Keka's chatbot capabilities are demonstrated hands-on as examples of HRIS-embedded chatbots. Building a simple HR FAQ chatbot using no-code platforms (TARS, Kommunicate, or IBM Watson Assistant) is a hands-on project — giving students the experience of designing, building, and testing a functional HR chatbot without programming. LLM-powered HR chatbots (built on ChatGPT API or Claude API via LangChain RAG) are introduced as the more sophisticated tier — demonstrating how a chatbot can answer natural language HR policy questions by retrieving relevant sections from the employee handbook. HRIS workflow automation using Darwinbox, SAP SuccessFactors, and Workday workflow tools is covered: automating onboarding task assignment, probation period tracking, document collection workflows, appraisal cycle notifications, and off-boarding checklists. Employee experience platform evaluation (Leena AI, Medallia, ServiceNow HR Service Delivery) is included for HR professionals working in larger enterprises.
The HR chatbot design framework is covered from the ground up: intent mapping (what questions do employees actually ask HR, and in what volumes?), conversation flow design, escalation logic (when should the chatbot hand off to a human?), and integration with HRIS systems for real-time data retrieval. Darwinbox's AI assistant and Keka's chatbot capabilities are demonstrated hands-on as examples of HRIS-embedded chatbots. Building a simple HR FAQ chatbot using no-code platforms (TARS, Kommunicate, or IBM Watson Assistant) is a hands-on project — giving students the experience of designing, building, and testing a functional HR chatbot without programming. LLM-powered HR chatbots (built on ChatGPT API or Claude API via LangChain RAG) are introduced as the more sophisticated tier — demonstrating how a chatbot can answer natural language HR policy questions by retrieving relevant sections from the employee handbook. HRIS workflow automation using Darwinbox, SAP SuccessFactors, and Workday workflow tools is covered: automating onboarding task assignment, probation period tracking, document collection workflows, appraisal cycle notifications, and off-boarding checklists. Employee experience platform evaluation (Leena AI, Medallia, ServiceNow HR Service Delivery) is included for HR professionals working in larger enterprises.
8
Ethical AI in HR, Compliance, Governance & Capstone Project
AI HR systems are among the most consequential AI applications in use today — they make or influence decisions that affect people's livelihoods, careers, and economic security. This makes ethical AI in HR not a theoretical concern but a practical, legal, and reputational imperative. This final module equips HR professionals to be the ethical governance layer in their organisation's AI HR deployments — and concludes with a capstone project that demonstrates end-to-end AI HR competence.
Algorithmic bias in HR AI is covered with case studies and real examples: Amazon's resume screening AI that systematically downgraded women's applications because it trained on historical male-dominated hiring data; hiring algorithms that discriminated against non-native name patterns; performance management AI that produced different results for minority groups. Bias detection and mitigation frameworks are taught practically: how to audit AI HR tools for disparate impact, what questions to ask vendors about training data and fairness testing, and how to implement human oversight checkpoints in AI-assisted HR workflows. India's Digital Personal Data Protection Act (DPDP Act 2023) and its implications for HR AI data processing, employee consent, and cross-border data transfers are covered in practical terms. The EU AI Act classification of HR AI systems as high-risk is explained, with its implications for Indian companies with EU operations or parent companies. AI governance frameworks for HR — the policies, audit procedures, and oversight mechanisms that responsible organisations implement around AI HR tools — are designed hands-on. The module concludes with a Capstone Project: each student designs and presents a complete AI HR transformation roadmap for a fictional or real organisation — covering tool selection, data requirements, bias mitigation, change management, and success metrics. This capstone is a portfolio piece that demonstrates strategic AI HR leadership to any employer.
Algorithmic bias in HR AI is covered with case studies and real examples: Amazon's resume screening AI that systematically downgraded women's applications because it trained on historical male-dominated hiring data; hiring algorithms that discriminated against non-native name patterns; performance management AI that produced different results for minority groups. Bias detection and mitigation frameworks are taught practically: how to audit AI HR tools for disparate impact, what questions to ask vendors about training data and fairness testing, and how to implement human oversight checkpoints in AI-assisted HR workflows. India's Digital Personal Data Protection Act (DPDP Act 2023) and its implications for HR AI data processing, employee consent, and cross-border data transfers are covered in practical terms. The EU AI Act classification of HR AI systems as high-risk is explained, with its implications for Indian companies with EU operations or parent companies. AI governance frameworks for HR — the policies, audit procedures, and oversight mechanisms that responsible organisations implement around AI HR tools — are designed hands-on. The module concludes with a Capstone Project: each student designs and presents a complete AI HR transformation roadmap for a fictional or real organisation — covering tool selection, data requirements, bias mitigation, change management, and success metrics. This capstone is a portfolio piece that demonstrates strategic AI HR leadership to any employer.
Hands-On Projects You Will Build
📊 People Analytics Dashboard
Full Power BI dashboard covering headcount, attrition, time-to-hire, diversity metrics, and engagement scores. Interactive filters by department, level, and tenure band.
🔮 Employee Attrition Prediction Model
ML model using real-world anonymised HR dataset. Identifies top 20% at-risk employees with SHAP-based explanation of individual risk factors. Actionable output for HRBP intervention.
🤖 HR FAQ Chatbot
Functional HR chatbot built on no-code platform. Answers 30+ common employee HR queries. Integrated with leave policy, payroll FAQ, and onboarding information.
💬 AI-Assisted Recruitment Workflow
End-to-end AI recruitment demo: AI JD creation, bias audit, AI sourcing strategy, resume screening configuration, and structured interview question bank generated with AI.
😊 Employee Sentiment Analysis Report
AI-powered analysis of 200 anonymised employee survey verbatim responses. Theme extraction, sentiment scoring, and executive summary generated using ChatGPT prompts.
🗺️ AI HR Transformation Roadmap (Capstone)
Strategic AI adoption plan for a target organisation — covering use case prioritisation, tool selection, bias governance, data requirements, and 12-month implementation timeline.
Career Opportunities & Salary After This Programme
People Analytics Manager / Specialist
₹12–22 LPA · High demand across all sectors
Designs and manages HR analytics systems and predictive models. One of the highest-value HR specialisations in India's current talent market. Found at every large employer.
AI HR Specialist / HR Tech Lead
₹10–20 LPA · Emerging premium role
Leads AI tool evaluation, implementation, and governance for the HR function. Bridge between HR and IT/AI teams. Commanding salary premium over traditional HR generalist roles.
Talent Intelligence Analyst
₹8–16 LPA · Recruiting specialisation
Uses AI talent platforms (Eightfold, LinkedIn Insights) to provide market intelligence for talent acquisition strategy. High value in competitive talent markets across Pune's tech and manufacturing sectors.
HR Business Partner (AI-augmented)
₹10–18 LPA · 40–60% salary premium
Traditional HRBP role enhanced by predictive analytics, AI engagement tools, and data-driven decision support. AI-capable HRBPs earn significantly more and advance faster than traditional counterparts.
CHRO / VP HR (AI-literate senior leader)
₹25–60 LPA · Senior leadership
AI literacy is becoming a baseline requirement for CHRO roles at tech-forward companies. Leaders who can build and govern AI-augmented HR functions are in extraordinary demand at board level.
HR Technology Consultant
₹12–25 LPA · Advisory track
Advises organisations on AI HR tool selection, implementation, and change management. High-value consulting track for experienced HR professionals with AI literacy and analytical skills.
Who Should Join This Programme?
- Working HR professionals — HR Generalists, HRBPs, Talent Acquisition Specialists, L&D Managers, HR Operations — who want to add AI skills and access the salary premium that comes with them
- Senior HR leaders — HR Managers, HR Heads, CHROs — who need to understand AI HR technology deeply enough to lead its adoption, evaluate vendors, and govern its ethical use
- MBA HR students and fresh HR graduates who want to enter the job market with differentiated AI HR skills that traditional HR programmes do not provide
- Talent acquisition professionals who want to leverage AI tools for faster, more effective, and more equitable hiring
- HR consultants who advise organisations on HR technology and want to build AI HR advisory capabilities
- Business professionals and managers who work closely with HR and want to understand how AI is transforming people management in their organisations
The Aapvex Difference
HR-First, Not Tech-First: Many AI courses teach the technology first and then try to connect it to HR. The Aapvex AI HR Programme starts with HR use cases and problems — and then introduces the AI tools and techniques that address them. Every concept is taught in the context of real HR scenarios that practicing HR professionals will recognise immediately.
No Coding Required — But Genuine AI Competence: This programme does not ask you to learn Python or build ML models from scratch. But it does go far deeper than "here are some AI tools, swipe left to use them." You will understand how AI systems in HR actually work — well enough to evaluate them critically, audit them for bias, and govern their use responsibly. That depth of understanding is what makes the salary difference.
Aapvex's Unique HR + AI Cross-Domain Expertise: Aapvex has been Pune's leading HR training institute before expanding into AI training. This means our AI HR programme is built by trainers who understand both the HR domain and the AI technology — not by AI specialists who don't know HR or HR specialists who don't understand AI. Call 7796731656 to learn more.
Student Success Stories
"I was a Talent Acquisition Specialist at a mid-size IT company, spending 60% of my time manually screening resumes and scheduling interviews. The Aapvex AI HR Programme completely changed how I work. The AI recruitment module gave me practical skills I applied immediately — I configured our ATS with AI screening rules, set up LinkedIn Talent Insights for competitive intelligence, and built a Power BI dashboard our CHRO now presents to the board. Most importantly, I rebuilt my resume highlighting AI HR skills and was offered an AI-augmented TA Lead role at a top Pune IT company at ₹15 LPA — up from ₹9 LPA. The ROI on this course was immediate and dramatic. Call 7796731656 and ask for details — this programme will change your career."— Meghana P., AI-augmented TA Lead, IT Company, Pune (promoted from ₹9 to ₹15 LPA)
"As an HR Manager with eight years of experience, I was worried that AI would make my role obsolete. The Aapvex programme transformed that anxiety into confidence. I now understand exactly what AI can and cannot do in HR — which means I can lead our company's AI HR adoption rather than fear it. The attrition prediction model we built in Module 4 was a revelation — I presented it to our CHRO and we are now implementing a similar model in Darwinbox. The ethics module was equally important — I am now our company's go-to person for AI HR governance. My salary was revised upward to ₹22 LPA after I led our first AI HR implementation. This programme is essential for any senior HR professional in India today."— Rahul D., HR Manager (People Analytics Lead), Manufacturing Company, Pune
Batch Schedule & Learning Options
- Weekend Batch: Saturday & Sunday, 4–5 hours/day. Designed for working HR professionals. Most popular format — fills every month. Completes in 10–12 weeks.
- Weekday Batch: Monday–Friday, 2 hours/day. Best for MBA students and HR professionals between roles. Completes in 10–12 weeks.
- Live Online Batch: Real-time Zoom with screen-sharing and tool demos. Same trainer, curriculum, and placement support. Ideal for HR professionals in any city across India.
- Corporate Group Training: Customised AI HR training programmes for HR teams of 5 or more. Please call 7796731656 for corporate enquiries.
Maximum 15–20 students per batch. Call 7796731656 or WhatsApp 7796731656 to check batch dates and reserve your seat.
Frequently Asked Questions
Do I need a technical background to join the AI HR Professional Programme?
No technical background is required. This programme is designed for HR professionals — it assumes HR domain knowledge but zero technical or programming background. We teach you exactly the AI concepts and tools you need without requiring you to write a single line of code. Logical thinking, comfort with data in Excel, and genuine curiosity about AI are all you need.
What is the fee for the AI HR Professional Programme at Aapvex?
The AI HR Professional Programme starts from ₹15,999. EMI options are available. Call 7796731656 for current batch pricing and any active discounts.
How is AI HR different from regular HR Analytics?
Traditional HR analytics describes the past using Excel and dashboards — how many people left last quarter, what was our time-to-hire. AI HR uses machine learning to predict the future and automate decisions — which employees are most likely to leave in the next 6 months, which candidates are most likely to succeed in a role, how will our headcount need to evolve with business growth. This course covers both levels, starting from analytics fundamentals and advancing to predictive AI systems.
What salary premium can I expect after the AI HR Programme?
HR professionals with demonstrated AI and analytics skills typically earn 40–80% more than their peers without these skills. People Analytics Managers in Pune earn ₹12–22 LPA. AI HR Specialists earn ₹10–20 LPA. HR leaders who can govern AI HR deployments are among the highest-paid HR professionals in Indian organisations today. Many of our graduates have reported salary increases of 50–100% within 6–12 months of completing the programme.
Is ChatGPT covered for HR use cases in this course?
Yes — extensively and practically. Module 6 is dedicated to Generative AI for HR: using ChatGPT, Claude, and Microsoft Copilot for job description creation, interview question generation, HR policy drafting, performance review templates, and employee communication. You will build a personal library of 15 HR prompt templates that you can use immediately in your current role.
Does the course cover AI bias and ethics in HR?
Yes — as a dedicated full module. Module 8 covers algorithmic bias in HR AI (with real case studies of AI systems that discriminated in recruitment and performance management), bias detection and auditing frameworks, GDPR and India's DPDP Act compliance for HR data, AI governance policy design, and the HR professional's ethical responsibility in AI deployment. This is increasingly a required competency for any HR leader managing AI HR tools.
Will I learn to build an attrition prediction model?
Yes. Module 4 guides you through building an employee attrition prediction model step-by-step using Python in a guided template format — no prior Python knowledge needed. You will load real-world HR data, train a classification model, evaluate its accuracy, and generate a scored list of at-risk employees with SHAP-based explanations of each individual's risk factors. This is the single most impressive AI HR capability you can demonstrate to any employer or in any CHRO conversation.
What AI HR tools like Darwinbox and HireVue are covered?
Darwinbox AI, Keka AI, SAP SuccessFactors AI features, Workday AI, LinkedIn Talent Insights, HireVue video interviewing AI, Eightfold AI for talent intelligence, Pymetrics for neuroscience-based assessment, SeekOut for diverse talent sourcing, Power BI for HR analytics, and Leena AI / Culture Amp for employee sentiment. Both HRIS-embedded AI tools and specialised AI HR products are covered.
Is this course suitable for freshers or only for experienced HR professionals?
Both. Experienced HR professionals (2+ years) benefit most from modules 3–8, where the AI tools and analytics are applied directly to HR problems they already understand well. MBA HR students and freshers benefit from the complete programme — it gives them a differentiated skill set and salary trajectory that traditional HR graduates without AI skills cannot match. Call 7796731656 and we will advise you honestly on whether your background is a good fit.
How do I enrol in the AI HR Professional Programme?
Call or WhatsApp 7796731656, fill out our Contact form, or walk into our Pune training centre for a free counselling session. We will match you to the right batch based on your background, goals, and schedule.