AI in HR

Why Should HR Care About AI?

Traditional HR work faces several problems that AI can help solve:

• Massive resume volume — popular positions may receive hundreds or thousands of applicants, reading each one individually takes enormous time
• Unconscious Bias — humans tend to judge based on name, gender, age, or educational institution without realizing it
• Repetitive tasks — answering the same questions repeatedly, such as "How many leave days do I have left?" or "What benefits are available?"
• Lack of insights — many HR departments know employees are leaving but don't know "why" or "who will leave next"
• Unfocused talent development — sending employees to training courses that don't match their actual Skill Gaps

AI can help solve these problems by processing large volumes of data in a short time and finding patterns that humans cannot see. However, AI also has significant limitations — let's explore how AI can help HR.

How Can AI Help HR? — 6 Game-Changing Areas

1. Automated Resume Screening (AI Resume Screening)

This is one of the most popular AI use cases in HR. The AI system works as follows:

• Read and extract data from CVs — whether PDF, Word, or image, AI can extract key information such as work experience, skills, education, and certifications
• Match with Job Description — compare candidate qualifications with position requirements using Natural Language Processing (NLP), not just traditional Keyword Matching
• Rank candidates — score suitability and prioritize so Recruiters can focus on the highest-potential candidates first
• Reduce screening time by 70-80% — from 23 hours per position down to just minutes

2. HR Q&A Chatbot

An HR Chatbot is AI that serves as an "automated HR service center" for employees, answering frequently asked questions such as:

• "How many vacation days do I have left?"
• "How do I file a medical expense claim?"
• "What is the Work From Home policy?"
• "When is payday?"
• "What is the maternity leave process?"

Chatbots can answer these questions 24/7 without waiting for HR to be in the office, reducing repetitive workload for the HR team by 40-60%, freeing HR to focus on strategic work.

3. Sentiment Analysis from Employee Surveys

When organizations conduct Employee Engagement Surveys or Pulse Surveys, open-ended written responses are often neglected because there are too many to read. AI can help by:

• Analyze emotions and feelings (Sentiment) — categorized as Positive, Negative, Neutral
• Extract key topics (Topic Extraction) — such as "benefits," "supervisors," "compensation," "work environment"
• Detect trends — compare survey results across time periods to see if satisfaction is increasing or decreasing
• Alert danger signals — if severe negative sentiment is detected in a particular department

4. Turnover Prediction

This is one of AI's most powerful use cases in HR — using Machine Learning to analyze employee data and predict who is likely to resign before it actually happens.

Data that AI uses for analysis includes:

• Tenure — employees who have been in the same position for 2-3 years without promotion are at high risk
• Leave history — changing leave patterns, such as more frequent absences on Mondays/Fridays
• Performance reviews — employees with good performance but lack of recognition
• Structural changes — change of supervisor, team, or location
• Compensation data — compared against market averages
• Historical Exit Interview data — reasons why former employees resigned

AI builds a model from this data and assigns a Risk Score for each employee, enabling HR to take timely preventive measures (Retention Strategy) such as having conversations with employees, adjusting compensation, or changing job responsibilities.

5. Training Path Recommendations Based on Skill Gaps

AI can analyze employees' current skills compared to required skills for their current position or desired career path, then recommend appropriate training courses.

• Build Skill Profiles — from evaluations, certifications, and project history
• Identify Skill Gaps — which skills are missing or weak
• Recommend Learning Paths — prioritize courses in the right sequence
• Track Progress — measure whether employees are actually improving

This approach ensures training budgets are spent effectively, rather than sending employees to training using a "cast the net wide" approach.

6. Performance Review — AI-Assisted Feedback Writing

Many managers feel that writing Performance Reviews is difficult and time-consuming. AI can help by:

• Gather KPI data — pull quantitative performance results from the system
• Draft initial feedback — write a draft that managers can edit further
• Check for bias — alert if language tends toward bias, such as praising women differently from men
• Recommend Development Goals — based on evaluations, suggest development targets for the next cycle

Comparison Table: What AI Can Do vs What Still Needs Humans

Not every HR task can be replaced by AI — some tasks AI does better than humans, some still require human decisions, and some work best when AI + humans collaborate:

HR Task	AI Can Do	Still Needs Humans	AI + Human
Initial CV screening	✓
Interview and assess Culture Fit		✓
Turnover analysis / Resignation prediction			✓
Hiring decisions		✓
Answer benefits / policy questions	✓
Analyze Engagement Survey			✓
Recommend Training Path			✓
Write Performance Reviews			✓
Mediate disputes / Provide counseling		✓
Workforce Planning			✓

Noteworthy AI Tools for HR

There are currently several popular AI tools for HR:

Tool	Core Capability	Best For
LinkedIn Recruiter AI	AI recommends suitable candidates, auto-generates candidate outreach messages	Recruitment / Talent Sourcing
HireVue	Video Interview + AI analyzes answers and body language	Initial interviews
Workday AI	Turnover Prediction, Skill Gap Analysis, Workforce Planning	Large organizations using Workday
BambooHR	Employee Self-Service, Performance Management, Analytics	SMEs to mid-sized organizations
Eightfold AI	Talent Intelligence Platform — screen CVs, analyze Skills, Career Pathing	Organizations wanting AI-first HR
Pymetrics	Assess Soft Skills through games + AI analyzes results	Selection focused on Culture Fit

Most of these tools are Cloud services (SaaS) requiring monthly/annual subscriptions, and employee data is sent to the cloud for processing — which leads to important PDPA concerns discussed next.

Critical Cautions — What HR Must Know Before Using AI

1. AI Bias in Recruitment

AI learns from historical data — if historical data is biased, AI will replicate that bias. The most famous example is Amazon in 2018, which developed an AI resume screening tool but found that AI scored female applicants lower than males because 10 years of historical hiring data was predominantly male.

Types of AI Bias found in HR:

• Gender Bias — gender prejudice, such as scoring male applicants higher for engineering positions
• Age Bias — age prejudice, such as automatically eliminating long-graduated applicants
• Education Bias — institutional prejudice, such as overweighting prestigious universities
• Name Bias — prejudice from names that indicate race, religion, or region

2. PDPA and Employee Personal Data

The Personal Data Protection Act (PDPA) has been fully enforced since 2022 and directly affects AI usage in HR:

• Consent required — before sending employee data to AI for analysis, the purpose must be disclosed and explicit consent obtained
• Necessity principle — collect and process only necessary data, not everything
• Data subject rights — employees have the right to view, correct, or delete their personal data
• Cross-border data transfer — if AI is a Cloud service with servers abroad, legal protection measures are required
• Penalties — fines up to 5 million baht and/or imprisonment up to 1 year for serious violations

3. Transparency — Employees Should Know When AI Makes Decisions

One of the key principles of Responsible AI is Transparency. Employees and job applicants have the right to know:

• Whether AI is involved in the selection process
• What data AI uses to make decisions
• Whether they can request a human review if they disagree with AI results

The European Union (EU) has issued the EU AI Act, which classifies HR AI as "High-Risk AI" requiring rigorous auditing and reporting — although Thailand does not yet have AI-specific legislation, PDPA already covers these issues to some extent.

Saeree ERP and the HR Module

Saeree ERP has a comprehensive HR module for workforce management:

• Employee Master Data — personal history, education, experience, certifications
• Payroll — calculate salary, taxes, social security, provident fund
• Leave Management — sick leave, personal leave, vacation, maternity leave with approval workflow
• Benefits — medical expenses, travel allowances, other benefits
• Performance Evaluation — set KPIs, evaluate by individual/department
• Time Attendance — connect with fingerprint/card scanners
• HR Reports — employee statistics, turnover rates, personnel costs

Case Studies: Organizations That Successfully Use AI in HR

Let's look at examples of world-class organizations that have concretely adopted AI in HR:

Unilever — Reduced Hiring Time by 75%

Unilever uses AI in its Graduate Program recruitment process by having applicants play games (Gamified Assessment) where AI analyzes Cognitive Ability and Personality, followed by AI-assisted Video Interviews. The result: reduced hiring time from 4 months to 4 weeks, with a 16% increase in diversity among selected candidates.

IBM — 95% Accurate Turnover Prediction

IBM developed an internal AI called the "Predictive Attrition Program" that analyzes hundreds of employee data variables and predicts resignations with 95% accuracy, enabling managers to take retention measures before employees submit their resignation — IBM estimates this system saved $300 million over 3 years.

Before Starting to Use AI in HR — What Do You Need First?

Adopting AI in HR is not just about buying and installing tools. Organizations must have a strong foundation first:

Foundation	Details	Why It Matters
1. Organized HR Data	Complete, accurate employee data stored in digital systems (not paper or scattered Excel files)	AI needs clean, organized data — if data is bad, results are bad (Garbage In, Garbage Out)
2. Clear HR Processes	Have SOPs for recruitment, evaluation, development, and separation	AI follows processes — if processes aren't clear, AI can't help
3. ERP/HRIS System	Centralized HR data storage and management system	The primary data source that AI will draw from for analysis
4. Data Governance Policy	Define who can access what data, how long it's kept, and what it can be used for	Essential for PDPA Compliance and preventing data misuse
5. Personnel Readiness	An HR team with a basic understanding of AI — knowing what AI can and cannot do	To use AI effectively and wisely, not blindly trust everything AI produces

Conclusion: AI in HR — It Really Works, But Must Be Used Wisely

AI is a high-potential tool for HR, but it is not a magic solution that can fix everything. What matters most:

• AI excels at repetitive, high-volume tasks (e.g., screening CVs, answering questions)
• AI analyzes deep insights that humans cannot see (e.g., Turnover Prediction, Sentiment Analysis)
• But AI should not make critical decisions alone (e.g., hire/don't hire, terminate)
• Must take AI Bias and PDPA concerns seriously
• Having a good ERP/HRIS system is an indispensable foundation before building on with AI

AI will not replace HR, but HR professionals who use AI will replace those who don't — the best preparation is having an organized HR data system starting today, so you're ready for AI when the time comes.

- Saeree ERP Consulting Team

If your organization needs a comprehensive HR system as a foundation for future AI adoption, you can schedule a Demo or contact our consulting team for further discussion.