Generative Artificial Intelligence (AI) has become the buzzword in the world of technology for almost a year ever since Open AI, a US-based AI research and deployment company introduced Chat GPT, an AI tool for testing to the public. However, the technology behind it is not new, as the history and evolution of generative AI dates back to the late 1950s when researchers were focused on rule-based systems which could simulate human thinking. The 1990s saw an exponential shift in generative AI with the introduction of neural networks.

The last decade saw significant strides in generative AI, leveraging new ML algorithms to create varied content like text, sounds, images, and 3D models. Neural networks discern patterns in existing data, fueling potential for industry transformation, heightened productivity, and improved customer experiences.

Although HR trailed in AI adoption, generative AI's ability to swiftly produce hyper-personalised content across an employee's journey stands to revolutionise talent decisions and amplify business impact. Here are specific applications across the employee lifecycle that harness data for impactful talent decisions.

Anticipating the talent

The success of any strategic workforce planning depends on accurate demand forecasting. However, forecasting demand is not limited to predicting the right numbers but also identifying the right skill/competency mix to ensure that it generates the results that are expected. There are AI-enabled tools that help sift through past data, analyse trends, and predict future needs based on past demand. However, generative AI facilitates continuous learning, adapting to changing market dynamics, customer preferences, and organisational limitations. These learnings are incorporated to ensure a proactive approach to workforce planning and development. 

Attracting the talent

Generative AI, a subset of deep learning, streamlines resume processing on ATS/job portals. By quickly parsing and scoring skills, it expedites candidate stack ranking, reducing recruiter effort. Moreover, it aids in identifying high-performing employees by creating customised skill match scores.

For prospective candidates, generative AI tools enhance the application experience, providing prompt application status, fitment scores, personalised feedback, and recommending suitable open positions. In screening calls, chatbots conduct personalised discussions with multiple candidates simultaneously, processing responses instantly to qualify candidates efficiently.

Generative AI extends support to interview panels with structured questionnaires, aiding in shortlisting the most suitable candidates. Post-selection, addressing the notice period challenge, a pre-onboarding engagement using voice bots ensures effective communication and engagement, handling various tasks from documentation to queries on policies and benefits. This technology offers a seamless transition from offer acceptance to the first day on the job.

Developing the talent

Performance management as a concept has evolved from just an achievement of goals to a measurement of the impact that it has created on the business. Hence the success of any developmental strategy would be to develop the potential of the employee which can directly relate to actions/output which will help achieve the business goals. The success of any type of performance management would depend on using generative AI tools/systems which will help in building their potential by way of identifying the competency gaps and creating a hyper-personalised learning solution which can be converted to a performance solution by way of integrating with the business strategy and dovetail into their individual development plan (IDP). 

Deploying the talent

Generative AI helps in analysing the project requirements and matching it with resource availability by evaluating the skill sets of the employees based on their qualifications, training, and applied experience. By a personalised evaluation of a member’s skills/competencies, they can align each employee to the appropriate projects. Generative AI-based tools can also help in resource allocation by considering workload, resource gaps, availability of resources and project timelines thereby reducing project risks, enhancing operational efficiency, and increasing productivity.

Engaging the talent

Engagement surveys are mostly a reactive strategy to effect changes in an organisation. Generative AI tools can help deal with this problem by using sentiment analysis by analysing employee interactions in different modes (e.g., emails, and chats). These tools can generate new text samples with sentiment characteristics like the training data. This is a major shift from the past where they classified texts rather than create new ones. These tools can help in monitoring employees’ behavioural patterns and engagement levels through data-backed insights in real time. This will help in identifying the red flags and proactively take remedial measures to create a conducive environment for engaging them.

Summing it up

The implementation of generative AI has the potential to revolutionise various segments of the HR function. However, we should also be cognizant of the potential risks that can accompany them with the likes of data bias, data security, privacy concerns, lack of monitoring & auditing, lack of a compliance & regulatory framework, and most importantly dealing with ethical issues. Organisations should create their own guidelines on the responsible and ethical use of generative AI till the time there are proper regulatory frameworks in place. 

The goal of AI is to create computers that can behave like humans and complete jobs faster than humans can do. As Stephen Hawking sums it up beautifully “We must develop as quickly as possible technologies that make possible a direct connection between brain and computer, so that artificial brains contribute to human intelligence rather than opposing it”. Generative AI will help us in achieving this goal and it is in our best interest as a function to adopt it at the earliest.