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How Generative AI transforms future-ready skilling strategies

Story • 10th Jul 2024 • 2 Min Read

How Generative AI transforms future-ready skilling strategies

Skilling#Future of Work#HRTech#HRCommunity#Artificial Intelligence

Author: Charles Nwoke Charles Nwoke
951 Reads
As HR leaders focus on evaluating and adopting HR technologies, particularly generative AI in 2024, it becomes crucial for business leaders and HR professionals to understand how this technology can enhance future-ready skilling strategies.

As businesses move forward in an ever-changing environment amid the transformative technological age and a growing skills gap, the development of key skills heavily depends on the best strategies adopted by organisations. 

Organisations are gradually shifting from the traditional method of work to adopting technological toolkits such as generative AI. Generative AI is changing the way businesses are run and the nature of work by augmented learning. 

Gen AI is paving the way for opportunities, workforce learning, and inclusive change. It enables us to build our skills, receive information, discover new opportunities and explore divergent career paths. There are no limitations to your ability to acquire new skills and augment learning with generative AI tools. Generative AI is instrumental in the learning and skill-building of the workforce. 

Since HR leaders are prioritising assessing and gathering HR techs, especially generative AI in 2024, there is a need for business leaders and HR professionals to know how generative AI can augment future-ready skilling strategies.

Utilising generative AI to prepare for future skills

Generative AI utilizes detailed learner data to assess competencies and learning styles, crafting personalized content while ensuring relevance, minimizing information overload, and fostering practice opportunities.

It also enables continuous updates, adaptations, and expansions of training content. Despite 69% of L&D professionals trained in content creation, only 3% currently employ tools for curation. However, Generative AI can automate tasks, freeing up L&D professionals for higher-value cognitive work. By analyzing employee data, it predicts potential skills gaps, enhancing organizational readiness and career decision-making.

Generative AI designs realistic, immersive training and gamification simulations that dynamically respond to user decisions and adapt training content for cultural and language contexts. This interactive approach provides instant feedback, automates assessments, and answers questions in real-time.

Moreover, Generative AI acts as a career coach, guiding employees in skills development and decision-making. For instance, Harvard University integrates this technology into its Computer Science 50 course, offering real-time feedback and solutions to students' queries.

You can also read: 

  • What is Loud Learning and why is it essential for employee upskilling
  • Poly-employment: Everything you need to know about this Gen Z workplace trend
  • What do women want at the workplace?

The human aspect of generative AI

There are many concerns about generative AI. The main concern is fear of job loss. For example, what would be the role of a writer when a Gen AI tool starts writing? L&D leaders need to find the right mix of skills and technology for the tasks in their role.

Also, since Gen AI is based on available data, it raises privacy and data protection concerns. The ethical aspects of Generation AI are constantly being debated, especially when it comes to the fine line between personal and organisational data. Existing data may contain biases and there is a risk of perpetuating them. 

There is a need for HR managers and leaders to learn and benefit from AI-augmented skills. All of us are on the stream of learning and sharing various experiences, achievements and concerns will enable society to embrace and partner with AI in the proper way. 

It is essential to work very closely with line managers when defining learning needs and encouraging participation in learning. Continuous monitoring and validation of AI results through feedback mechanisms are essential to ensure the quality of training content.

Read More

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