Administrasi HR 15 min read

Generative AI in HR: Transforming the Future of Work

By Jordhi FarhansyahPublished 29 Sep, 2023 Last updated 20 March 2024

Generative AI is a part of artificial intelligence (AI) tools that can learn on its own. With the vast development, it has been used in many industries like creative industry, IT, and even HR professionals as it continues to evolve.

Generative AI can produce multiple types of content, including text, image, audio, and even codes.

As of today, generative AI can create content in response to our own language request. It doesn’t need certain codes that need to be learned. With

So, how generative AI can transform the way professionals work? Let’s discuss it with the article down below.

Understanding how generative AI works

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Learning about generative AI models

Generative AI, also known as generative artificial intelligence, refers to a class of machine learning models and algorithms designed to generate data that is similar to, or in some cases indistinguishable from, real data.

These models are used in various applications, including natural language processing, computer vision, and creative content generation.

Generative AI has shown a significant rise with the introduction of ChatGPT in late 2022. Launched by OpenAI, it became a popular generative AI chatbot model because of its amazing result and capability of human-like responses.

Exploring generative AI’s underlying mechanisms

Here’s a simplified overview of how generative AI works.

Data collection and preprocessing

The first step in training a generative AI model is to gather a large dataset of real-world examples of the type of data you want the model to generate.

For example, if you want to generate text, you would collect a dataset of text documents. This data is then preprocessed to remove noise, irrelevant information, or any inconsistencies.

Model architecture selection

There are various generative AI model architectures to choose from, depending on the type of data you are working with. Some popular generative models include:

  • Variational Autoencoders (VAEs): VAEs are used for generative tasks in various domains. They consist of an encoder that maps input data to a latent space and a decoder that maps points in the latent space back to data space.
  • Generative Adversarial Networks (GANs): GANs consist of two neural networks: a generator and a discriminator. The generator generates data, while the discriminator tries to distinguish between real and generated data. They are trained adversarially, with the generator trying to improve its ability to fool the discriminator.
  • Recurrent Neural Networks (RNNs) and Transformers: These models are often used for sequence generation tasks, such as text generation. RNNs have a temporal element, making them suitable for sequences, while Transformers are known for their parallel processing capabilities and have become popular for many generative tasks.

Training

During the training process, the generative model learns to capture the underlying patterns and structures in the training data.

In the case of GANs, for example, the generator tries to produce data that is increasingly difficult for the discriminator to distinguish from real data.

Sampling

After training, the generative model can be used to sample new data points.

For example, in the case of text generation, you would input a seed text, and the model would generate the next words or sentences based on the patterns it learned during training.

Evaluation

Generative AI models are evaluated based on various criteria, including the quality, diversity, and realism of the generated data.

Evaluation metrics depend on the specific task and may involve human judgment, as well as automated measures like perplexity (for text) or image quality metrics (for images).

Fine-tuning and iteration

Depending on the quality of the generated data and the specific task, the model may undergo fine-tuning or further iterations to improve its performance.

Prominent Generative AI Models

Basically, there are three prominent generative AI models that have been widely used by many people. Here are some of the models.

Dall-E: The AI Artist

DALL-E is a generative AI model developed by OpenAI. The name “DALL-E” is a combination of the famous artist Salvador Dalí and the character Wall-E from Pixar’s animated movie.

This model is renowned for its ability to create unique and creative images based on textual descriptions provided to it.

DALL-E works by converting words in a textual description into corresponding images. For example, if you provide a description like “an elephant with zebra stripes,” DALL-E can generate an image that matches that description.

This combines natural language understanding with impressive image generation capabilities.

ChatGPT: Conversational AI

ChatGPT is a generative AI model developed by OpenAI, based on the GPT (Generative Pre-trained Transformer) architecture.

ChatGPT is designed for engaging in natural human-to-machine conversations. The model has a broad understanding of language and can answer questions, talk about specific topics, and engage in dialogues with users.

ChatGPT is highly versatile and finds applications in various domains, including customer support, chatbots, virtual assistants, and more. It enables human-like interactions in text-based communication.

Bard: The Storyteller AI

Bard is a generative AI model designed for storytelling or text narrative generation. Models like this can be used to create fictional stories, articles, or narrative content based on initial input or provided themes.

They can use techniques like GPT to generate text that follows a cohesive and engaging storyline.

These three models showcase how generative AI can be utilized in various creative and communicative contexts.

They leverage machine learning capabilities to produce content relevant to user requests, whether in the form of images, text, or conversational interactions.

This advances the development of increasingly sophisticated and useful AI technology in a wide range of fields.

Generative AI’s relevance for CEOs and HR leaders

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The rise of generative AI’s power has made CEOs and many HR leaders start considering the use of it more and more in their strategic planning.

Recent Gartner poll from more than 2,500 executives found that around 38% of generative AI investments is used for the sole purpose of improving customer experience and retention.

It was followed by 26% for the need of revenue growth, 17% for cost optimization, and 7% for business continuity.

Why generative AI matters in the business world

Generative AI holds significant importance in the business world due to several reasons. Here are some of the reasons.

Automation and efficiency

Generative AI enables the automation of various tasks and processes, leading to increased efficiency.

This is particularly crucial in a business context where time is of the essence. By automating repetitive and time-consuming tasks, businesses can optimize their operations, reduce costs, and free up human resources to focus on more strategic and complex activities.

Innovation and creativity

Generative AI, especially in the form of models like GPT-3 and DALL-E, has demonstrated remarkable capabilities in generating creative content, including text, images, and more.

This has profound implications for innovation and content creation within businesses. It can assist in ideation, design, and even contribute to the development of new products or services.

Personalization and customer experience

In customer-centric industries, generative AI can be employed to personalize interactions with customers.

This ranges from chatbots providing tailored customer support to recommending personalized products or services based on individual preferences. This personalization enhances the overall customer experience and builds stronger customer relationships.

Data analysis and decision-making

Generative AI plays a crucial role in data analysis, helping businesses derive valuable insights from large datasets. This can inform strategic decision-making processes.

Whether it’s analyzing market trends, customer behavior, or internal operations, generative AI models can sift through vast amounts of data to provide actionable information, facilitating informed and data-driven decision-making.

Customer engagement and marketing

Generative AI models can be used in marketing strategies to create compelling content, generate ad copy, and even develop creative campaigns. Chatbots powered by generative AI enhance customer engagement by providing instant and personalized responses. This technology can contribute to more effective and targeted marketing efforts, ultimately driving customer acquisition and retention.

Competitive advantage

Businesses that adopt generative AI early and effectively integrate it into their operations can gain a competitive advantage.

Whether it’s through innovative product development, streamlined processes, or personalized customer experiences, leveraging generative AI can set a business apart in a rapidly evolving and competitive market.

Adaptability and scalability

Generative AI systems are adaptable to various domains and can scale alongside business growth.

Whether a business is a startup or an established enterprise, generative AI technologies can be customized to meet specific needs, making them versatile tools for organizations of all sizes.

Cost savings

Automation through generative AI can lead to cost savings by reducing the need for manual labor in repetitive tasks.

While there might be initial investment costs in implementing AI solutions, the long-term efficiency gains and cost savings often outweigh the upfront expenditures.

In summary, generative AI matters in the business world because it drives efficiency, fosters innovation, enhances customer experiences, facilitates data-driven decision-making, and provides businesses with a competitive edge in a dynamic and competitive landscape.

As technology continues to advance, businesses that harness the power of generative AI can position themselves for sustained growth and success.

Potential applications in HR functions

Generative AI has the potential to transform various HR functions by automating processes, enhancing decision-making, and improving the overall employee experience. Here are potential applications of generative AI in HR.

The potential applications of generative AI in HR are diverse and can contribute to making HR processes more efficient, data-driven, and employee-centric.

However, it’s important to implement these technologies responsibly, considering ethical considerations, privacy, and potential biases to ensure fair and equitable HR practices.

Use cases of generative AI in HR

Here are explanations for each of the listed use cases of Generative AI in Human Resources (HR).

Resume screening and candidate matching

Generative AI can streamline the initial stages of the recruitment process by automating resume screening and candidate matching.

The AI system can be trained on historical data of successful hires and can analyze resumes to identify key qualifications, skills, and experiences. It can then match candidates to job descriptions, ranking them based on their fit for the position.

This automation saves HR professionals significant time and helps ensure that candidates are more closely aligned with the job requirements.

For example, Mekari Talenta as one of HR software has Advance Recruitment feature that can simplify the recruitment process from resume screening to talent onboarding. Although this feature has not yet powered by AI, but it has the potential to utilize AI’s power to create automation and analysis based on data.

Automated content generation for job listings

Generative AI can assist HR departments in creating engaging and accurate job listings. By analyzing existing job descriptions and industry-specific language, AI can generate well-structured and appealing job postings.

This can attract a wider pool of applicants and improve the quality of applications, making it easier to find the right candidates.

Employee feedback analysis

Generative AI can be used to analyze employee feedback from various sources, including surveys, performance reviews, and chat logs.

Natural language processing (NLP) models can extract insights, sentiment, and trends from this data.

HR professionals can gain a deeper understanding of employee concerns, satisfaction levels, and areas in need of improvement.

This data-driven approach can inform HR strategies and interventions to enhance the workplace environment.

Personalized learning and development plans

Generative AI can help create personalized learning and development plans for employees. By analyzing an employee’s skills, performance data, and career goals, the AI can recommend specific training programs, courses, or resources.

This personalized approach ensures that employees receive relevant training opportunities that align with their career growth, enhancing their skills and contributing to their professional development.

Virtual HR assistants

Virtual HR assistants powered by Generative AI can provide 24/7 support to employees.

These AI-powered chatbots or virtual assistants can answer common HR-related questions, provide information on company policies and benefits, assist with onboarding processes, and guide employees through HR procedures.

They offer quick and consistent responses, improving HR service accessibility and efficiency while reducing the administrative burden on HR personnel.

Generative AI and the future of work

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Adapting to the changing landscape of HR

Generative AI has the potential to transform various HR functions by automating processes, enhancing decision-making, and improving the overall employee experience.

The potential applications of generative AI in HR are diverse and can contribute to making HR processes more efficient, data-driven, and employee-centric.

However, it’s important to implement these technologies responsibly, considering ethical considerations, privacy, and potential biases to ensure fair and equitable HR practices.

How generative AI can enhance HR operations

Generative AI can enhance HR operations in several ways, streamlining processes, improving decision-making, and providing a more personalized and efficient experience for both HR professionals and employees.

Here are ways in which generative AI can contribute to enhancing HR operations.

Automating routine tasks

Generative AI automates repetitive and time-consuming HR tasks such as resume screening, candidate matching, and even parts of the onboarding process.

As for this, HR professionals should embrace automation and focus on more strategic and value-added activities.

Training and upskilling in areas such as data analytics and AI management will be crucial.

Enhancing recruitment processes

Generative AI improves the efficiency of recruitment processes by analyzing resumes, creating engaging job listings, and matching candidates to job requirements.

HR teams should learn to leverage AI tools for recruitment tasks. This involves understanding the capabilities of AI models, interpreting their outputs, and incorporating human judgment in the final decision-making process.

Personalizing employee experience

Employee experience is a huge part in transforming how HR works. Generative AI contributes to personalized learning and development plans, creating tailored experiences for employees based on their skills, preferences, and career goals.

You should understand the individual needs of each employee and integrate AI-driven personalization into training and development programs. This requires a shift toward a more employee-centric approach.

Data-driven decision-making

Generative AI enables HR to make data-driven decisions by analyzing large datasets for insights into employee engagement, performance, and sentiment.

HR teams need to develop data literacy skills to interpret AI-generated insights. This involves understanding statistical concepts, data visualization, and using data to inform HR strategies and policies.

Improving employee engagement

Generative AI supports employee engagement initiatives by analyzing feedback, identifying areas of concern, and offering personalized solutions.

HR should use AI-generated insights to develop targeted engagement strategies. Regular communication and transparency are essential to ensure employees understand the role of AI in enhancing their experience.

Addressing bias in hiring

Generative AI can help reduce biases in hiring processes by identifying and eliminating biased language in job descriptions and supporting fair candidate evaluation.

HR professionals should actively address bias in AI algorithms, continuously monitor and audit AI systems, and ensure fairness and inclusivity in recruitment practices.

Facilitating remote work

Generative AI assists in managing remote work by providing virtual HR assistants, automating HR processes that support distributed teams, and offering personalized guidance.

HR needs to integrate AI tools that support remote work, ensure clear communication channels, and provide virtual support to employees navigating remote work challenges.

Ensuring ethical use of AI

Generative AI introduces ethical considerations such as privacy concerns, potential biases, and the responsible use of technology in HR.

Organizations should establish ethical AI guidelines, conduct regular audits of AI systems, and prioritize transparency and fairness in AI applications within HR.

Generative AI, when integrated thoughtfully into HR operations, can lead to more efficient, data-driven, and personalized HR practices.

It is essential for HR professionals to stay informed about emerging technologies, collaborate with AI specialists, and ensure that AI applications align with organizational goals and values.

Preparing for the future of HR with generative AI

Preparing for the future of HR with Generative AI involves a combination of upskilling, strategic planning, and ethical considerations. Here are some key steps and considerations.

Skill development

Data literacy: HR professionals should enhance their understanding of data analytics and data-driven decision-making. This includes learning to interpret and derive insights from data generated by AI systems.

AI fundamentals: Developing a foundational understanding of how AI and generative models work will be crucial. This involves learning about the principles behind these technologies, common use cases, and limitations.

Collaboration with data scientists: Foster collaboration between HR professionals and data scientists. This can involve joint training programs, workshops, or even hiring individuals with a background in both HR and data science.

Ethics and fairness

Bias mitigation: Learn about the potential biases that can arise in AI models and strategies to mitigate them. This is especially important in HR, where biased algorithms can impact recruitment, promotions, and other critical processes.

Ethical AI use: Develop guidelines for the ethical use of AI in HR. This includes considerations such as transparency, accountability, and fairness. Ensure that AI applications align with organizational values and legal standards.

Strategic planning

Aligning AI with HR goals: Clearly define how AI will support HR goals and initiatives. This might include streamlining recruitment processes, improving employee experiences, or enhancing talent development.

Change management: Prepare for organizational changes that come with the adoption of AI. Communicate the benefits of AI to employees, address concerns, and provide training to ensure a smooth transition.

Data security and privacy

Compliance: Stay informed about data protection regulations and ensure that AI applications comply with privacy standards. Implement robust data security measures to protect sensitive HR information.

Employee consent: Clearly communicate to employees how their data will be used and obtain informed consent for AI applications that involve personal information.

Continuous learning

Stay informed: The field of AI is rapidly evolving. HR professionals should stay informed about the latest developments, trends, and best practices in AI to ensure that their strategies remain current.

Professional development: Invest in ongoing professional development for HR teams. This might involve attending conferences, workshops, or online courses focused on AI in HR.

Pilot programs and testing

Start small: Implement pilot programs to test the effectiveness of generative AI in specific HR functions. This allows for iterative improvement and reduces the risk of widespread implementation without thorough testing.

User feedback: Gather feedback from HR professionals and employees during pilot programs. This input can help refine AI systems and ensure that they meet the specific needs of the organization.

Cultural adaptation

Cultural shift: Foster a culture of innovation and adaptability. Encourage a mindset that embraces the positive aspects of AI, emphasizing its role as a tool to augment human capabilities rather than replace them.

Inclusivity: Ensure that all employees, regardless of their technical background, feel included in the AI transformation. Provide training and support to bridge knowledge gaps.

Collaboration

Cross-functional collaboration: Encourage collaboration between HR, IT, data science, and other relevant departments. Cross-functional teams can work together to develop, implement, and monitor AI applications in HR.

Vendor partnerships: If using external AI solutions, collaborate with reputable vendors and ensure a strong partnership for ongoing support, updates, and alignment with organizational values.

By taking a proactive approach to preparing for the future of HR with Generative AI, organizations can ensure a smooth integration of these technologies while maintaining a human-centric approach to HR practices.

Continuous learning, ethical considerations, and strategic alignment will be key factors in navigating the evolving landscape of HR.

Benefits of generative AI in HR

Improved efficiency and productivity

Generative AI can significantly improve the efficiency and productivity of HR departments by automating time-consuming and repetitive tasks.

This includes tasks like resume screening, job listing generation, and responding to frequently asked HR-related questions.

With AI handling these routine activities, HR professionals can allocate more time to strategic tasks, such as talent acquisition, employee development, and organizational planning.

This leads to a more efficient use of human resources within the HR function.

Enhanced candidate and employee experiences

Generative AI can contribute to better candidate and employee experiences. For candidates, AI-driven processes can ensure a smoother and more streamlined recruitment process.

Candidates receive quicker responses and more personalized interactions, improving their perception of the company.

For employees, AI can assist with onboarding, provide instant access to HR information, and offer personalized learning and development opportunities.

All of these contribute to higher levels of satisfaction and engagement.

Data-driven decision-making

Generative AI enables HR departments to make data-driven decisions. By analyzing large volumes of HR data, AI can uncover insights and trends that might not be apparent through manual analysis.

For example, AI can identify patterns in employee feedback, turnover rates, or performance data, allowing HR professionals to make informed decisions about talent management, employee engagement strategies, and organizational improvements.

This data-driven approach enhances HR’s ability to make strategic decisions that benefit the organization.

Scalability in HR processes

Generative AI provides scalability in HR processes, making it easier to manage HR functions as organizations grow.

For instance, when dealing with a high volume of job applicants, AI-powered resume screening and candidate matching can handle a larger number of applications efficiently.

Similarly, virtual HR assistants powered by AI can provide consistent support to a growing workforce without a linear increase in HR personnel. This scalability allows HR departments to adapt to changing organizational needs and maintain service quality.

Notable examples of generative AI adoption in HR

Many companies across industries are leveraging the use of generative AI to improve their services and offerings. One of the mainly used generative AI tools that these companies used is ChatGPT.

Here are some of the case studies of companies who benefited from generative AI.

Microsoft’s Bing

Bing was one of the very first companies to leverage the use of ChatGPT. Collaborating with OpenAI, Bing has powered up their search engine using ChatGPT as per February 2023.

This is a smart move by Bing because it has preceded its rival, Google, before they announced Bard.

The Coca-Cola Company

As an international company, Coca-Cola has a team dedicated to AI and is already showing the utilization of AI in creative ways possible.

One of the  visible efforts was producing an ad where some famous artworks come to life by utilizing OpenAI’s DALL-E2 as a generative image tool. In that ad, Coca-Cola has reproduced Andy Warhol’s 1962 creation of Coca-Cola bottle.

Duolingo

Duolingo is another example of a company who has been leveraging ChatGPT to expand its features. Launched in March 2023, Duolingo offers Duolingo Max that is powered by GPT-4.

With this new subscription tier, it allows users to explore new language exercises based on the power of generative AI.

It allows users to chat with Duolingo’s chatbot and give an explanation of some answers that they got from the questions.

It also allows its users to level up their conversation skills in certain languages with simulated situations. This can provide similar experiences as if you have a real conversation in the real world.

Limitations and ethical considerations

The potential for bias in AI-generated content

Bias in AI-generated content refers to the presence of unfair or discriminatory elements in the output produced by generative AI models.

These biases can be unintentionally learned from biased training data or reflect societal biases present in the data used to train the model.

For example, if a generative AI model is trained on historical data that contains gender biases, it may inadvertently generate biased content in terms of gender representation or language.

As for impact, bias in AI-generated content can perpetuate and amplify existing societal biases, leading to unfair or discriminatory outcomes.

This is a significant concern, especially in applications like hiring, where biased algorithms can affect individuals’ opportunities and experiences.

Privacy concerns

Privacy concerns arise when generative AI models are trained on sensitive or personally identifiable information (PII). There’s a risk that the generated content might inadvertently reveal private details or patterns from the training data.

Let’s say, if a model is trained on healthcare records to generate medical text, there’s a risk of generating content that could indirectly reveal specific patient information.

Thus, this privacy breaches can lead to legal and ethical issues, erode user trust, and result in significant consequences for individuals whose private information is exposed.

Ensuring transparency and fairness

Transparency involves making the operations of AI models understandable and interpretable, while fairness pertains to ensuring that the AI system treats all individuals and groups fairly, avoiding discrimination.

If an AI model makes decisions about job applications, it should be transparent in how it reaches those decisions, and the decision-making process should not favor or disadvantage certain demographic groups unfairly.

Lack of transparency can lead to mistrust in AI systems, and unfairness can result in discriminatory outcomes. Both issues can have serious consequences, especially in applications with societal impact, such as hiring, lending, or law enforcement.

Addressing these limitations and ethical considerations involves adopting best practices and incorporating ethical guidelines throughout the development and deployment of generative AI models:

  • Diverse and Representative Training Data
  • Privacy-Preserving Technique
  • Explainability and Transparency
  • Fairness Measures
  • User Education
  • Regulatory Compliance
  • Ongoing Monitoring and Auditing

By proactively addressing these considerations, developers, organizations, and policymakers can contribute to the responsible and ethical deployment of generative AI technologies.

Conclusion

Generative AI has found applications in a wide range of domains, including text generation, image synthesis, music composition, and more.

These models have the potential to automate creative tasks and assist humans in various creative endeavors.

In addition to its use in many industries, generative AI is also transforming HR and has helped many companies for automating routine tasks, improving decision-making through data analysis, and enhancing the overall employee experience.

These use cases demonstrate how AI can add value to HR functions, allowing HR professionals to focus on more strategic and human-centric aspects of their roles.

In summary, adapting to the changing landscape of HR driven by generative AI requires a combination of upskilling, embracing automation, ensuring ethical AI practices, and maintaining a human-centric approach.

HR professionals who can effectively leverage the capabilities of generative AI while maintaining a focus on people will be better positioned to drive positive change within their organizations.

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Jordhi Farhansyah
Penulis yang selama 2 tahun terakhir fokus memproduksi konten seputar HR dan bisnis. Selain menulis, sehari-hari Jordhi juga aktif merawat hobinya di bidang fotografi analog.