Gen AI has something to teach us about skilling and growth

Generative AI promises to help solve the massive reskilling issue it creates by reinvigorating businesses’ approach to learning and development.

When it comes to reskilling, generative AI is a double-edged sword. On the one hand, businesses will soon find themselves in dire need of a workforce reskilling program, with gen AI set to disrupt 90% of jobs by 2032, according to our recent research. In just two years’ time, according to another study, executives believe nearly half the skills that exist in today’s workforce won’t be relevant because of AI.

On the other hand, generative AI also promises to help solve the massive reskilling issue it creates. With its ability to quickly analyze vast datasets, generative AI can help organizations create continuously updated knowledge management repositories, identify evolving skill trends and create adaptive and personalized learning content that ensures organizations stay ahead of industry changes.

Businesses are taking note. A recent study found a higher percentage of survey participants anticipating learning and development (L&D) technology budget increases (24%) in the next fiscal year than decreases (17%). This was significantly greater for high-performance organizations (39% increase vs. 4% decrease).

More than one in five respondents said generative AI would be an L&D technology priority for the next 12 months, and that number doubled among high-performance organizations.

Such enthusiasm is well-timed. According to BCG, businesses in general have not prioritized skill development to date. Despite leading companies spending up to 1.5% of their annual budgets on learning and skill building, according to the consultancy, their leaders do not discuss skill building in the same way they do other goals, including environmental, social and governance (ESG) goals. BCG found that 20% do not include any mention of learning or skill building, and in many others, the topic is only touched on in a generic paragraph on human resources or social responsibility.

With the introduction of gen AI across the business landscape, this needs to change. Not only will workers need to learn how to work with generative AI to make them more productive, but they’ll also need to learn new skills as the technology automates some of the tasks they’ve traditionally performed.

What gen AI can do for learning and skills

Here are four key ways in which generative AI can help businesses reinvigorate their learning and development programs to meet the skilling needs of today’s workforce:

• Create highly relevant content. A key element of generative AI is its ability to craft and continuously update engaging learning materials. This can range from storyboards and assessment questions to videos and interactive simulations. Using gen AI, businesses can create learning resources tuned to employee needs, such as e-learning, scenario-based exercises and gamified modules.
  
  In addition to the learning materials themselves, gen AI can generate layouts, wireframes and prototypes that facilitate the development of new L&D products and solutions that adapt to stay relevant.
  
  
• Personalize learning that adapts to individual needs. Gen AI-driven L&D materials can adapt as learners interact with it. This results in personalized learning paths that align with each learner's unique background and interests. With this personalized approach to learning, employees are more likely to engage with the content, retain what they learned, and gain needed skills.
  
  Gen AI uses various techniques to ensure skill acquisition by flattening the "forgetting curve." These include customized, spaced repetition learning experiences and gamification strategies that reinforce learning.
  
  Businesses can also use generative AI’s automated translation capabilities to offer materials in various languages, resulting in a more inclusive learning environment.
  
  
• Offer live help with chatbots and virtual assistants. Gen AI-enhanced virtual assistants can guide learners through complex learning journeys. For instance, virtual assistants can help learners find relevant resources, set goals and track progress. Conversational interfaces add a “human touch” when learners need on-demand support and real-time feedback.
  
  The productivity gains from gen AI assistants can be significant for new associates, who can quickly develop expertise that otherwise could take months.
  
  
• Summarizing text and generating code. Gen AI can distill large datasets into concise, high-quality summaries. This enables fast creation of condensed, comprehensive content. With gen AI’s code generation and code review capabilities, L&D professionals can also ensure high-quality code that is easily maintained

Preparing for gen AI-based skilling

To succeed with generative AI in learning and skilling, businesses need to address several key elements:

1. A structured career architecture plan: Organizations should develop a role-based skill dictionary. This will help them define the specific learning and skilling goals they want to achieve with gen AI, such as personalized learning, content creation, skill assessment or assisted/automated tutoring.
   
   
2. Integrated data and processes: Businesses need to integrate their HR systems to ensure that high-quality, relevant and diversified datasets are available for AI model training and fine-tuning. The data should include performance measurement plans, annual review scores, recognized training needs by managers, and a variety of learning materials tied to competencies and responsibilities relevant to developing a future-ready business.
   
   
3. An employee-centric design: Employee-friendly interfaces and experiences will encourage employees to adopt generative AI learning systems. This includes gamifying learning, intuitive dashboards, seamless integration with existing learning management systems and personalized learning paths. Personalized recommendations and adaptive learning paths can significantly enhance the learning experience.
   
   
4. Feedback and continuous improvement: Learners should be empowered to provide continuous feedback to help improve gen AI learning systems. It’s crucial for businesses to make regular updates and enhancements based on user feedback and technological advancements.
   
   
5. Attention to privacy: Robust privacy measures are needed to protect sensitive data and ensure regulatory compliance. This is especially important when dealing with employee performance data.
   
   
6. Evaluation and metrics: Organizations should evaluate the performance of these personalized learning programs and measure the impact on business performance. Successful results will enhance acceptance and adoption.

A robust governance system is critical to drive adoption, measure business impact and provide continuous feedback to employees and executives.

The future of reskilling with gen AI

By some estimates, the average half-life of skills is now less than five years, and in some tech fields, it’s as low as 2.5. With the integration of generative AI and a “human in the loop” model, businesses can ensure resilience and continuity in an era defined by constant change.

This article first appeared on LinkedIn.