Generative AI and the Future of Recruitment

Emerging technology is creating lots of potential to improve the hiring process.

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May 2, 2023

While modern AI natural language processing (NLP) models may not be able to interpret Chewbacca’s Wookiee language from Star Wars (yes, unfortunately confirmed after testing), they are capable of understanding multiple human languages.

That should come as no surprise these days because it wasn’t all that long ago that we first learned to communicate with virtual assistants like Cortana, Siri, and Alexa via smartphones before the technology scaled to multiple devices and industries. Now, as we reflect on the past and look to the future, it’s time to learn how to communicate with today’s AI, which can now have open-ended conversations and will transform the way we work.

Indeed, PwC conducted a study estimating that the economic impact of AI could reach up to $15.7 trillion globally by 2030. And Geoffrey Hinton, widely regarded as the Godfather of AI, believes that we are currently experiencing one of the greatest technological advancements in history, comparable in scale to the Industrial Revolution.

We’re already seeing the impact. With the emergence of generative AI models, there have been significant advancements in machine learning, which in turn has led to major breakthroughs in NLP.

One example of this includes advanced AI language models using the GPT (Generative Pre-Trained Transformer) architecture. In short, AI language models use deep-learning neural networks that are designed to mimic the complex structure and function of the human brain. In that vein, depending on system design, conversational AI tech generally uses language models to analyze large language datasets to predict the probability of each potential next word in response, using probability distributions to provide human-like responses.

It’s worth pointing out, too, that looking back over time, technological innovation has resulted in more jobs created than eliminated, and the jobs that remained have adapted or transformed. It will, however, be imperative to become comfortable with learning skills outside of the general scope of talent acquisition in order to adjust and leverage tech as it advances.

To thrive in an AI-enabled world, organizations must invest in skill development and become agile. In a survey by Deloitte on Human Capital Trends, 76% of executives mentioned that an employee’s ability to adapt and learn new skills is one of the most important factors for organizations to navigate through disruptions of tomorrow.

Additionally, according to a McKinsey & Company report, social and emotional skills such as communication and empathy, as well as cognitive skills like critical thinking and creativity/innovation, will be essential for success in the workplace of the future.

On top of those important skills, CEOs will also want their employees to learn how to use these emerging AI capabilities as they realize the potential increase in productivity they can have in the workplace.

Talent Acquisition Use Cases With AI

Visual job ads. Marketing professionals have already started experimenting with text-to-image AI to run campaigns that highlight their products and services. Similarly, talent acquisition can create customized visuals via natural-language inputs to showcase what it’s like to work at a company and provide a glimpse of a day in the life based on the role.

Outreach personalization. Sales practitioners are already using AI to craft personalized messages to prospective customers. Likewise, talent acquisition professionals can also start to customize messages using AI — not substituting but supplementing communication, while allowing more time to focus on personal human interaction and relationship-building.

Sourcing. Prospect generation starts with research, of course. After gathering the right intel, TA can use AI to confirm the best channels for finding talent, categorize required qualifications by key skills, organize search strings in Boolean form, and rank search strings based on provided criteria.

Creating compelling job descriptions. Research has shown for quite some time that candidates do not spend much time reading job descriptions. AI can help craft captivating, concise, easy-to-the-eye job descriptions that can highlight culture to encourage candidates to apply.

Addressing Common AI NLP Concerns

Diversity and bias. Like humans, AI language models have been found to exhibit biases as they are trained on datasets that reflect the biases inherent in society. James Dean, Google’s head of AI, acknowledges that the effectiveness of machine learning models depends largely on the input data used during training.

Machine learning models have the potential to become less biased than humans through proper training and feature selection. And so to reduce bias, with the proper team, organizations must consider having inclusive datasets that represent a diverse range of perspectives, regularly review the feature selection process, and continually monitor the performance of the AI algorithm.

Privacy. Talent acquisition should always keep personal and confidential data top of mind. Think of this as if your input is being used for public consumption when communicating with conversational AI. To ensure responsible AI data privacy practices, companies should provide clarity by establishing policies around information shared with AI chatbots. There could be potential need for additional forms of regulation.

Accuracy. We currently do not always completely understand why AI chatbots sometimes make invalid statements. This is also known as “artificial hallucinations,” which is when AI responds in a convincing way but the content is entirely made up and incorrect.

To confront this issue, researchers and developers are exploring a variety of approaches, including more sophisticated NLP models and the use of explainable AI (XAI) techniques to better understand how chatbots arrive at their decisions.

AI will not only increase demand for individuals who can build it; it will also create the need for individuals to work alongside it. In today’s world for TA, this means sharpening our soft skills while learning to work with AI to augment tasks that are repetitive. In addition, regular assessments will be essential to identify skills of the future to sustain a competitive advantage and keep pace with AI transformations that will continue to evolve the way we work.

AI will also allow TA to focus more on innovation and being a strategic partner, while maintaining positive hiring-manager and candidate experiences.

While some concerns about AI might be valid, the technology is improving at a rapid pace and the cost of not using it is far greater. Companies must be forward-thinking, addressing these concerns while also embracing the opportunities that AI can achieve.

As Bill Gates said, we should take advantage of this current breakthrough in AI even with its imperfections, as they will be reduced over time.

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