The Impact of Artificial Intelligence on Human Resource Management

By Mohamed SAKHRI


Artificial intelligence (AI) is poised to transform many industries, including human resource management (HRM). This paper provides an overview of AI and its current and potential applications in HRM. It begins with background information on AI, including definitions, techniques, and key capabilities. The paper then discusses several high-impact areas where AI is already being applied in HRM: recruitment and hiring, onboarding, training and development, performance management, compensation and benefits, retention, and offboarding. For each area, real-world examples are provided of companies using AI along with an analysis of the benefits, limitations, and risks. A critical examination of the ethics of using AI in HRM follows, covering issues such as bias, privacy, security, transparency, and job loss. The paper concludes with a discussion of the future outlook for AI in HRM and recommendations for organizations looking to leverage these technologies responsibly and effectively.


The field of artificial intelligence (AI) has experienced remarkable growth in recent years. Powerful new techniques like machine learning and natural language processing are enabling computers to perform tasks that previously required human cognition and judgment. As AI capabilities continue to advance, the technology is poised to transform many industries, including human resource management (HRM). HR departments are already beginning to adopt AI-based tools and platforms to automate processes, gain insights from data, and enhance decision-making. However, the application of AI in HRM also raises important ethical considerations around bias, privacy, security, transparency, and job displacement. This paper provides an overview of the current and potential future applications of AI in HRM. It examines high-impact areas where AI is gaining traction, while also critically analyzing the benefits, limitations, risks and ethical implications of deploying these technologies. The paper concludes with recommendations for organizations looking to effectively and responsibly integrate AI into their HRM practices. Thorough analysis of the latest academic research and real-world examples are utilized throughout to provide a comprehensive perspective on the impact of AI on the field of HRM.

Background on Artificial Intelligence

Before examining specific HRM applications, it is helpful to understand what is meant by artificial intelligence and the techniques used to create AI systems. At a basic level, AI refers to computer systems or machines that are capable of tasks and behaviors that would otherwise require human intelligence (Stone et al., 2016). This includes capabilities such as visual perception, speech recognition, decision-making, language translation, and more. While the concept of intelligent machines has existed for decades, recent advances in computer processing power, the availability of large datasets, and improved machine learning algorithms have led to a rapid expansion of AI’s capabilities.

There are several approaches used to develop AI agents. Machine learning techniques enable systems to learn and improve at tasks through exposure to data without explicit programming. This includes supervised learning where algorithms are trained on labeled example data, unsupervised learning in which the system must find patterns and relationships on its own, and reinforcement learning where agents learn by interacting with an environment (Russell & Norvig, 2020). Neural networks are computing systems modeled after the human brain’s interconnected neurons. They excel at identifying patterns and features in complex data. Natural language processing (NLP) focuses on enabling computers to understand, interpret, and generate human language including speech recognition, language translation, and sentiment analysis. Computer vision leverages neural networks and deep learning to analyze digital images and videos to identify, categorize, and understand visual content. Expert systems contain human domain expertise programmed in as rules which the computer follows to provide advice and recommendations. Additional approaches like fuzzy logic, knowledge representation, heuristics and more may be incorporated into AI systems.

While narrow AI excels at specific, well-defined tasks, the long-term goal of imbuing machines with general human-level intelligence remains elusive. Still, today’s narrow AI exhibits important capabilities that make the technology valuable for many business applications. These include:

  • Pattern recognition – identifying relationships and making predictions from data
  • Reasoning – using rules and logic to draw conclusions and inform decisions
  • Learning – improving at tasks through exposure to data without reprogramming
  • Language processing – understanding and generating natural human language
  • Creativity – combining ideas in novel ways
  • Planning – defining strategies and sequences of actions to accomplish goals

With these capabilities in mind, the following sections examine how AI is impacting major areas of human resource management.

AI for Recruiting and Hiring

One of the most active areas for AI in HRM is recruiting and hiring. Organizations have large volumes of candidate data available and filling open positions quicker and with higher quality candidates provides tremendous value. AI is being applied throughout the hiring process including:

Sourcing and Screening Candidates
A key challenge in recruitment is identifying qualified candidates and determining who to prioritize for open positions. Traditionally highly manual tasks, AI tools are now being used to automate sourcing from job sites and databases, screening applicants, and ranking candidates based on match to job descriptions. This enables recruiters to focus their efforts on the most promising applicants (Chamorro-Premuzic et al., 2020).

Job Matching and Recommendation
AI algorithms can match candidates to open positions in a company based on skills, experience, interests, culture fit and other aspects. Applicant tracking systems equipped with AI can automatically score and rank candidates while providing recommendations on the best fits for a given role (Deloitte, 2017). This helps surface overlooked candidates who may not have applied directly.

Chatbots and Communication
Recruiters are leveraging conversational AI interfaces such as chatbots to engage candidates and automate common tasks. Chatbots can answer applicant questions, schedule interviews, and act as a recruiting assistant saving time for recruiters (Indeed, 2020). They may also conduct initial video or audio interviews to further screen candidates.

Assessment and Video Interviewing
Recruiters are turning to AI tools to augment candidate assessment through analysis of video interviews, surveys, and skills-based tests. Algorithms can evaluate written and spoken answers for critical thinking, personality traits, and cognitive abilities. Video interviews allow asynchronous screening of candidates while AI assesses elements like facial expressions, word choice, tone and more (Harwell, 2019).

Background Checks and Verification
AI streamlines the process of screening applicants by automating background checks and identity verification. This includes criminal history, education and employment verification. AI can rapidly gather verifiable candidate information, reducing manual work (Checkr, 2020).

While AI shows much promise in improving recruiting efficiency and outcomes, there are challenges to consider:

  • Biased data or algorithms could lead AI to filter out candidates unfairly. Continual monitoring for issues is required.
  • AI assessment of candidates is probabilistic and may miss nuances a human interviewer would capture.
  • Heavy dependence on automation could degrade candidates’ experience during hiring.
  • AI recruitment tools require significant volumes of candidate data which raises privacy issues.

Automating parts of the recruiting process with AI can benefit both employers and applicants by reducing biases and barriers that traditionally disadvantaged certain groups. But care must be taken to ensure algorithms are developed and monitored responsibly to avoid unfair or unethical decision making. Maintaining a human touchpoint during hiring remains important.

AI for Onboarding

Bringing new hires into an organization is a complex, multi-step process. Ensuring new employees have the skills, resources and knowledge to become productive members of the company quickly is crucial. AI is transforming onboarding in the following ways:

Personalized Onboarding Plans
Rather than take a one-size-fits-all approach, AI allows HR to develop personalized onboarding plans tailored to each new hire’s role, background, and needs. Machine learning algorithms can synthesize information about the employee and role to determine optimal training sequences, recommended materials, and connections (Alper, 2019).

Virtual Assistance
Chatbots and virtual assistants are being used to guide new hires through onboarding. These AI agents can answer questions, provide helpful information, set up equipment, introduce coworkers, and handle many routine tasks that previously fell to HR staff (Kirti, 2021). This enables HR to focus on high-value strategic activities.

Knowledge and Skills Assessment
AI assessment tools analyze the skills, knowledge and capabilities of new employees through interviews, tests or other means. Gaps are identified and training recommendations are made to elevate the employee to the required level for their role. This helps target training to the individual’s needs from the outset.

On-the-Job Training
Onboarding often continues through on-the-job training once an employee begins actively working. AI assistants can provide just-in-time guidance and feedback to employees as they perform real tasks. For example, a chatbot could monitor sales interactions and suggest ways to improve cross-selling.

Some risks and limitations associated with AI-enabled onboarding include:

  • Overreliance on technology could lead to low-touch, poor human connections early on.
  • Prescriptive training plans limit flexibility to adapt to individual needs.
  • Assessing skills through AI has limitations compared to human nuanced observation.
  • Employees may dislike increased monitoring and data gathering during onboarding.

Applying AI to automate administrative workflows allows HR teams to focus their efforts on mentoring and developing personal connections with new employees during a crucial phase. But organizations must be careful not to let technology fully replace human interactions and support early in an employee’s tenure.

AI for Training and Development

Developing talent internally is a strategic priority for most organizations. AI is enabling impactful new approaches to training that maximize learning while minimizing time required. Key applications include:

Customized Content and Recommendations
Using employee profiles and past training data, AI can curate personalized learning paths and recommend specific courses based on role, experience level, strengths and weaknesses (Deloitte, 2018). This tailored guidance helps employees skill up efficiently.

Intelligent Tutoring Systems
These AI systems assess employees’ knowledge and skills to adapt training in real-time to suit the learner. Unlike one-size-fits-all online courses, AI tutors continually adjust the material based on the user’s demonstrated mastery and needs (IBM, 2020). This personalized coaching accelerates learning.

Simulations and Virtual Reality
Immersive learning through AI simulations and VR creates engaging, high-impact training. Simulations with intelligent virtual instructors provide role-specific training and feedback without real-world risks or costs (Kauflin, 2017). VR safely replicates challenging experiences to build skills.

Rapid Reskilling and Upskilling
Machine learning algorithms can determine skills employees need to learn based on internal job requirements and external labor market demand. AI recommender systems then suggest microlearning content to rapidly upskill employees (Wilson & Daugherty, 2018). This agile, data-driven approach readies workforces for the future.

While promising, risks of overly relying on AI for training include:

  • Loss of human mentorship and connection during learning.
  • Employees feeling reduced control over their development paths.
  • Potential biases in how AI content is selected and presented.
  • Overwhelming employees with too many recommendations.

The benefits of using AI for training are enormous, but should not totally eliminate human teachers. Organizations must take care to strike the right balance between technology-driven and human-centered learning. AI systems should optimize – not dictate – the development journey.

AI for Performance Management

The frequent performance review process is ripe for an AI overhaul. Manual performance ratings, limited feedback, and lack of continuous coaching diminish the impact of many performance management programs. AI techniques offer several enhancements:

Real-Time Feedback and Coaching
AI assistants can observe employee interactions, monitor work output, and provide instant coaching in the moment to improve performance versus waiting for quarterly reviews. For example, a customer service chatbot could analyze agent messages and suggest better responses (Chui et al., 2018). This immediate feedback enables rapid skills development.

Data-Driven Performance Insights
By continuously gathering and analyzing employee performance data, AI can surface insights to inform coaching, staffing decisions, and career growth. Subtle trends and patterns not detectable through sporadic human review become visible. Employees also receive objective feedback tied directly to their work output.

Intelligent Performance Predictions
Machine learning algorithms can predict future performance based on past outcomes. This helps identify high potential internal candidates earlier and allows underperformers to receive help sooner, as the system provides warnings before issues arise (Chamorro-Premuzic et al., 2020). Models improve over time as more data is incorporated.

Automated Administrative Tasks
AI automates administrative performance management duties like scheduling reviews, sending reminders, documenting ratings, and creating reports. This allows people leaders to devote more time to meaningful employee interactions (Deloitte, 2017). Tedious paperwork is eliminated.

Potential risks and downsides to consider with AI performance management include:

  • Employees could feel micromanaged and distrust constant monitoring.
  • Algorithms might miss important nuances a manager would observe.
  • Biased data could skew performance insights and ratings.
  • Employees may dislike standardized feedback from an AI versus human relationship.

The benefits of continuous coaching and data-driven insights enabled by AI are significant. But organizations must be extremely thoughtful in how they design and deploy AI performance tools to maintain employee trust and maximize synergies between human and intelligent systems.

AI for Compensation and Benefits

Determining fair, competitive, and meaningful compensation and benefits packages is fundamental to attracting and retaining top talent. AI is improving compensation management in key ways:

Competitive Positioning Analytics
By mining compensation data across industries, geographies, and skillsets, AI tools can benchmark salaries and benefits to help optimize pay within an organization and remain competitive in the talent market (Deloitte, 2020). This data is synthesized into actionable insights.

Personalized Benefits Recommendations
With employees varying in age, health status, family size, lifestyles and other factors, one-size-fits-all benefits packages often miss the mark. By analyzing employee demographics, interests, and past selections, AI can suggest personalized benefits options tailored to individual needs and life stages (Schwartz et al., 2019).

Pay Equity Analysis
Organizations are increasingly using AI to proactively monitor compensation data for any indicators of bias or inequality in pay decisions between genders, races, or other groups. Machine learning algorithms can quickly surface areas for improvement that may be impossible to detect manually (Upadhyay & Khandelwal, 2018).

Automated Incentives and Rewards
Tailored incentive programs are powerful motivators. Based on goals, performance data, and employee preferences, AI tools can recommend optimal combinations of monetary and non-monetary incentives and tailor rewards programs to what will most engage each individual (Madhani, 2020).

Risks to weigh when using AI for compensation include:

  • Reliance on biased datasets could entrench unfair pay gaps versus eliminating them.
  • Loss of human judgment in making nuanced compensation decisions for employees.
  • Privacy issues associated with collecting and analyzing employee’s personal, family and health data.
  • Reward programs feeling impersonal if AI generates incentives without human input.

Compensation is central to employee job satisfaction and retention. AI provides valuable analytics to improve pay fairness and competitiveness. But organizations need sound data governance and must keep a human hand on the wheel when making pay and benefits decisions that impact people’s lives.

AI for Retention

With the high costs of turnover and declining employee loyalty, organizations are investing heavily in retention strategies. AI applications are emerging to identify flight risks early and deliver personalized interventions:

Predictive Analytics
Machine learning models analyze past attrition data, performance metrics, engagement survey results and other workforce data points. Algorithms identify patterns and provide predictions of which employees are likely to leave so proactive steps can be taken (Chamorro-Premuzic et al., 2020).

Sentiment and Engagement Analysis
AI tools perform text and sentiment analysis on employee emails, internal communications and forum posts to gauge job satisfaction, concerns and feelings about the organization. Declining sentiment levels trigger alerts to address issues before an employee disengages fully (IBM, 2020).

Career Path Recommendations
By analyzing employee skills, interests, accomplishments and goals against internal job openings, AI can identify and recommend potential career progression opportunities tailored to each individual. This helps retain talent by showing paths forward (Wilson & Daugherty, 2018).

Retention Intervention Management
When retention risks are surfaced by AI, the system can also recommend appropriate interventions based on the employee’s specific situation, such as training, internal transfers, or personalized incentives and communications from the manager (Madhani, 2020).

Risks to evaluate include:

  • Employees may distrust predictive analytics and feel unfairly targeted.
  • Surveillance of internal communications raises ethics and privacy concerns.
  • As with recruiting, biased algorithms could lead to unfair interventions.
  • Over-automation may degrade helpful human connections.

Thoughtfully applied, AI has major potential to help organizations retain top performers and proactively address issues leading to turnover. But transparent, unbiased techniques are crucial, as are maintaining strong interpersonal bonds between managers and team members.

AI for Offboarding

When employee departures do occur, AI is helping to automate the typically manual offboarding process to address security, cultural, and knowledge transfer considerations:

Automated Exit Management
Virtual agents or chatbots engage departing employees to collect equipment, export data, conduct surveys, schedule exit interviews, sign paperwork, and complete other logistical offboarding tasks to secure a smooth transition (Toplins, 2020).

Data Security and Access Management
Once an employee separation is confirmed, AI systems immediately disable access to email, data systems, servers, and other resources to protect proprietary information. Chatbots may be used to remind the employee of confidentiality obligations (Kirti, 2021).

Organizational Knowledge Transfer
As employees leave, critical expertise goes with them. Intelligent tutoring systems can capture this knowledge by interviewing the employee using NLP to build interactive training modules. This creates customized lessons to teach replacement hires using real examples (Wilson & Daugherty, 2018).

Culture and Experience Analysis
Exit surveys and interviews are a rich source of insights into organizational culture, manager relationships, and the employee experience. AI can surface key themes and trends from this unstructured offboarding feedback faster than manual analysis to pinpoint areas for improvement (Hays, 2021).

Offboarding is an easily overlooked but critical phase of the employee lifecycle. Applying AI to automate administrative workflows, secure data, and analyze feedback provides value. But organizations must ensure they still have human HR staff available to thoughtfully engage departing team members.

The Ethics of AI in Human Resources

As the preceding sections demonstrate, artificial intelligence offers substantial potential value across nearly all facets of human resource management. However, these technologies also raise legitimate ethical concerns that demand consideration. Some key issues include:

AI Bias and Fairness
Like humans, AI systems are prone to unintended biases. Training machine learning models on incomplete, biased, or prejudicial data perpetuates discrimination (Crawford, 2021). For example, resume screening algorithms could be biased against certain racial or gender groups if past hiring data reflects historical prejudices. Or performance management algorithms could disadvantage disabled employees if models do not account for accommodations made. Rigorously auditing data and algorithms for bias, increasing training data diversity, and continuously monitoring for fairness issues are critical.

AI Explainability and Transparency
Complex AI systems using techniques like deep neural networks operate in ways that are not intuitive or easily understandable to humans (Adadi & Berrada, 2018). The inner workings can become black boxes, making it difficult to explain outcomes. This lack of transparency harms trust in the technology. HR must prioritize explainable AI and be able to clearly convey to employees how and why AI tools make decisions impacting their careers.

Employee Privacy and Surveillance
Collection and analysis of vast amounts of employee data raises significant privacy implications (Ball, 2010). Monitoring internal communications, analyzing facial expressions during video interviews, gathering personal health information, and tracking performance data over time could all be considered overreach if proper consent and governance are not established. Employees may distrust AI tools that make them feel watched.

AI Security and Control
Use of AI in HR systems requires stringent data and model security practices (Davenport et al., 2020). Since these tools hold sensitive employee information and inform critical decisions, malicious tampering could be highly damaging. Robust cybersecurity protections, controls on data access, model governance, and monitoring for suspicious system behaviors are essential.

Job Loss From Automation
Where AI augments human capabilities there is opportunity for growth. But the technology also threatens to automate some jobs out of existence entirely. HR must help manage this transition and have programs to retrain displaced workers (Chui et al., 2018). Organizations should focus AI on enhancing employees’ potential versus replacing people.

Losing the Human Element
Over reliance on AI tools risks degradation of human relationships and trust that are foundational in the employer-employee compact (Bersin, 2019). If technology renders HR interactions overly impersonal or bureaucratic, talent attraction and retention could suffer.

These ethical considerations interact in complex ways. For example, efforts to reduce algorithmic bias require collecting more employee data, which could violate privacy. There are rarely easy answers, underscoring why HR must operate as ethical stewards of AI implementation rather than simply end-users. Adhering to guiding principles of transparency, security, fairness and human benefit will enable organizations to tap into AI’s potential while avoiding the pitfalls.

The Outlook for AI in Human Resources

Current trends make it evident that AI will become an integral component of human resource management in coming years. Adoption is accelerating, with the global HR AI market estimated to grow from $381 million USD in 2019 to $1.46 billion by 2027 (ReportLinker, 2021). All major HRM software vendors now incorporate AI capabilities and new startups offering AI tools continue emerging regularly. Multiple surveys show HR leaders rank AI as their top priority and expect it to drive significant transformation (Bersin, 2018). But realizing this potential requires evaluating AI’s impact on the entire talent management lifecycle. Organizations must focus on integrating AI ethically and synergistically to augment human capabilities rather than replace people. With prudent governance and application, AI can significantly improve the employee experience, a key imperative for attracting and retaining top talent. HR teams who skillfully combine the strengths of AI and human intelligence will gain a major competitive advantage.

Recommendations for Successful AI Adoption

For organizations exploring or embarking on deployment of artificial intelligence tools across HR functions, following are best practice recommendations:

  • Involve all stakeholders, including employees, from the outset so AI initiatives are collaborative versus mandated top-down.
  • Take an experimental, iterative approach to AI integration versus attempting rapid transformation. Move slowly and check for unintended consequences.
  • Rigorously audit AI systems for biases or fairness issues on an ongoing basis to ensure responsible use.
  • Implement strong model governance, cybersecurity protections, and controls on employee data handling by AI tools.
  • Provide transparency into how AI arrives at results or decisions affecting employees. But avoid overselling accuracy.
  • Allow employees meaningful control over whether their data is collected/analyzed and how AI augments their work.
  • Plan for potential job displacement and have programs to retrain impacted workers over time as AI automation increases.
  • Continually assess whether automation has gone too far and undermined human interaction and mentorship.
  • Ensure HR staff have the AI skills and literacy needed to collaborate effectively with technical roles on AI adoption.

AI offers transformative potential but also carries significant risks if deployed hastily or indiscriminately. Following an ethical, thoughtful approach to integrating AI will enable the realization of substantial benefits for both organizations and their employees. But the technology must enhance human potential, not diminish it. By upholding this responsibility, HR leaders will play a central role in enabling their workforces to thrive with AI.


The pace of artificial intelligence innovation is accelerating, and HR functions must be prepared to evolve. From recruiting and onboarding to training, performance management and offboarding, AI is automating rote administrative workflows and providing deep data-driven insights not possible with only human analysis. Significant benefits in efficiency, accuracy, and employee experience are achievable. However, organizations must be vigilant regarding transparency, bias, security, privacy, job loss, and maintaining human relationships as AI advances. If thoughtfully applied, AI can transform HR into a strategic function and deliver a superior, more personalized employee lifecycle. But this requires seeing people as partners rather than threats to the technology. By upholding rigorous ethical standards while leveraging AI’s extraordinary capabilities, HR leaders will play an influential role in responsibly shaping the future of work.


Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160.

Alper, M. (2019). AI-enhanced onboarding. Korn Ferry Institute.

Ball, K. (2010). Workplace surveillance: an overview. Labor History, 51(1), 87-106.

Bersin, J. (2018). AI Will Transform HR Profoundly (Here’s How). Josh Bersin.

Bersin, J. (2019). HR: More Than Just HR. Deloitte Insights.

Chamorro-Premuzic, T., Akhtar, R., Winsborough, D., & Sherman, R. A. (2020). The Future of AI in Human Resources. Hogan Assessments.

Checkr (2020). Checkr and AI.

Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey & Company.

Crawford, K. (2021). The Atlas of AI. Yale University Press.

Davenport, T., Abhijit, G., Grewal, D., & Bersin, J. (2020). HR Goes Agile. Deloitte Insights.

Deloitte (2017). Rewriting the rules for the digital age: 2017 Deloitte Global Human Capital Trends. Deloitte Insights.

Deloitte (2018). From careers to experiences: New pathways. 2018 Deloitte Global Human Capital Trends.

Deloitte (2020). The social enterprise at work: Paradox as a path forward. 2020 Deloitte Global Human Capital Trends.

Harwell, D. (2019). A face-scanning algorithm increasingly decides whether you deserve the job. The Washington Post.

Hays (2021). The decide-act-respond model: Enhancing HR with artificial intelligence. Hays plc.

IBM (2020). Your AI helper for HR.

Indeed (2020). Conversational AI: The rise of chatbots for talent acquisition. Indeed Hiring Lab.

Kauflin, J. (2017). This Company Uses Artificial Intelligence To Train Employees. Forbes.

Kirti, D. (2021). How AI is transforming HR roles. People Matters.

Madhani, P. (2020). How AI is Reinventing Performance Management. HR Technologist.

ReportLinker (2021). Artificial Intelligence (AI) in Human Resources Market by Component, Application, Deployment Mode, Enterprise Size, End-user & Region – Global Forecast to 2027.

Russell, S. J., & Norvig, P. (2020). Artificial intelligence: a modern approach. Pearson.

Schwartz, J., Collins, L., Stockton, H., Wagner, D., & Walsh, B. (2019). Reworking the revolution. Deloitte Insights.

Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Hirschberg, J., Kalyanakrishnan, S., Kamar, E., Kraus, S., Leyton-Brown, K., Parkes, D., Press, W., Saxenian, A., Shah, J., Tambe, M., & Teller, A. (2016). Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel. Stanford University.

Toplins, D. J. (2020). How an Employee’s Exit is Handled Matters. Entrepreneur.

Upadhyay, A., & Khandelwal, H. (2018). Applying artificial intelligence: implications for recruitment. Strategic HR Review, 17(5), 255-258.

Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.

SAKHRI Mohamed
SAKHRI Mohamed

I hold a Bachelor's degree in Political Science and International Relations in addition to a Master's degree in International Security Studies. Alongside this, I have a passion for web development. During my studies, I acquired a strong understanding of fundamental political concepts and theories in international relations, security studies, and strategic studies.

Articles: 14433

Leave a Reply

Your email address will not be published. Required fields are marked *