10 Use cases for AI in HR (with actual examples) - Part 1
Let's explore actual applications of AI in HR that will change the way we work.
In the rapidly evolving landscape of human resources (HR), the integration of artificial intelligence (AI) has emerged as a transformative force. AI technologies have the potential to revolutionize HR processes, enabling organizations to streamline operations, improve decision-making, and elevate employee experiences. While there is an abundance of advice and theoretical frameworks surrounding AI in HR, it is the real-world use cases that truly illustrate the tangible benefits and demonstrate the power of this technology.
Too often, discussions about AI in HR remain abstract, with vague notions of improved efficiency or enhanced productivity. However, to truly understand the potential impact of AI in HR, it is essential to explore concrete examples of its application across various domains. By delving into specific use cases, we can appreciate how AI empowers HR professionals to make data-driven decisions, automate repetitive tasks, and drive organizational success.
In this article, we will present ten compelling use cases that highlight the practical value of AI in HR. Each use case will illustrate how AI technologies have been leveraged to address critical HR challenges, such as talent acquisition, employee engagement, learning and development, performance management, and more. By examining these real-world examples, HR practitioners and organizational leaders can gain valuable insights, inspiration, and a clearer understanding of how AI can revolutionize HR practices in their own context.
Talent Acquisition
This marriage has been going on for years, and it has become even more relevant as remote work has enlarged the potential pool of candidates for every position. If you’re lucky, you get a ton of applicants. But you’re also going to need help sourcing and screening the right ones and keeping them engaged throughout the process. Say no more! AI to the rescue! You’ve got the combination of language and a high volume of data, so this looks like a great fit for LLMs that can help you with:
Resume screening: AI-powered systems can analyze resumes and applications, using natural language processing and machine learning algorithms to identify relevant qualifications and shortlist candidates. See https://vervoe.com/ai-in-resume-screening/ for more details on how this works.
Example: CVviz.Candidate sourcing: AI tools can search online platforms, social media, and professional networks to identify potential candidates based on specific job requirements and desired skills.
Examples: SeekOut, Source.hr. (full disclosure: I am an advisor to Source.hr)Chatbots for candidate engagement: AI-driven chatbots can provide automated responses to candidate inquiries, schedule interviews, and assist with initial screening, improving the candidate experience and streamlining the hiring process.
Examples: Paradox Olivia, Phenom.
Talent Development:
Sometimes the talent you need is already in your organization. But it takes so much effort to develop that talent. And it’s not just about creating training content: a successful talent development process requires personalization, evaluation, and follow-up. And with everybody in HR being so busy, who has time for that, right? AI can definitely help with that. You could have your own personal learning coach, that builds your learning path and supports your development path, even creating learning simulations.
Personalized learning: AI can analyze employee skills, learning patterns, and performance data to recommend personalized learning content and development paths.
Examples: Gloat, Microsoft Viva.Adaptive learning platforms: AI-powered platforms can dynamically adjust learning materials and assessments based on individual progress and skill gaps, optimizing learning outcomes.
Example: MyBites.io.Simulation-based learning: AI can be leveraged for simulation-based training so that new employees can practice and develop behaviors in a safe, controlled environment, getting them client-ready and reducing the burden on your existing team.
Example: Zenarate.Virtual reality (VR) training: AI and VR technologies can simulate realistic training scenarios, allowing employees to practice skills in a safe and immersive environment.
Examples: Transfr, Vrainium.AI-powered coaching platforms: Virtual coaching assistants can provide personalized guidance, feedback, and resources to employees, helping them improve specific skills or overcome challenges.
Examples: Rocky.ai, CoachHub’s Aimy.
Employee Benefits:
Benefits selection and recommendation: AI tools can analyze employee data, preferences, and usage patterns to provide personalized recommendations for benefits packages that align with individual needs.
Example: Nayya.Benefits administration: AI-powered platforms can automate benefits enrollment, track eligibility, manage claims, and provide employees with self-service options for benefits-related inquiries.
Example: HealthAtScale.
Total Rewards
This segment of the industry is pretty mature and has been using AI for a long time, with tools that aggregate and analyze compensation surveys, suggest compensation levels according to skill, experience, and markets, identify employees at flight risk, and recommend raises.
Compensation analysis: AI algorithms can analyze market data, employee performance, and other relevant factors to provide insights for determining competitive and fair compensation packages.
Examples: Payscale, Salary, Beqom.Performance-based rewards: AI systems can analyze performance metrics and provide recommendations for performance-based incentives and recognition programs.
Example: Forma.ai
HR Operations:
Employee self-service: AI-driven chatbots or virtual assistants can handle routine HR inquiries, such as leave requests, policy clarifications, and general HR information. There are standalone tools and also embedded features within existing platforms, and you can even build your own! Examples: Emilabs.ai, Espressive, Leena.ai.
Data analytics and insights: AI can analyze large volumes of HR data to identify trends, patterns, and insights related to workforce planning, employee engagement, attrition, and diversity metrics.
Coming up next
In part 2 of this article, I will focus on use cases for:
Skills Management
Workforce Planning
Employee Performance Management
Employee Engagement & Sentiment Analysis
Compliance & Risk Management
Stick with me on this further exploration of AI‘s impact on HR!
