Generative A.I. Can Transform Workforce Development: The Future of Corporate Training

Upskilling and reskilling are new-age slang for the long-standing corporate training industry. Companies invest billions annually in workforce development programs that improve employee well-being by teaching new technologies, sharpening soft skills like decision-making and problem-solving, and engaging high performers with educational opportunities. In 2021, Amazon committed $1.2 billion to train 300,000 employees as part of its Upskilling 2025 Pledge after reporting that almost 70 percent of Amazon employees interested in upskilling said they’d consider seeking employment opportunities with other companies offering training programs that skilled workers need to compete in today’s labor market.

While human resources departments at companies of all sizes undoubtedly understand the value of support services and job training associated with workforce development, few actually track the outcomes of these initiatives, and even fewer track the ROI. In fact, a recent survey by D2L indicated that only 33 percent of learning leaders measure the impact of learning programs in a financial sense. This lack of data sets has led to a dearth of developing new modalities for workplace learning. Without metrics, you can’t measure the impact of something—and it becomes hard to justify the effort and expense to experiment and optimize. As a result, most organizations, including the ones that care deeply about developing their staff, rely on industry standards such as subscriptions to large catalogs of asynchronous online courses. This reality holds otherwise innovative and pioneering companies back from creating a competitive advantage with workforce development initiatives. 

With the rapid advancement of artificial intelligence, a subsequent widening skill gap across virtually every role, and a growing body of cutting-edge research on the science of learning, it’s time to reexamine how learning happens at work—and how the use of A.I. in workforce development benefits the future of work.

Generative A.I. systems are poised to transform more than just content development, from delivery formats to user interfaces, assessment methodologies and beyond. As the CEO and co-founder of a company that marries learning science and A.I. technologies to help corporate training programs become more contextualized, measured and applied, I’ve seen the impact of A.I. on business processes firsthand. When applied to workforce development, the benefits of A.I. include creating hyper-engaging, real-time personalized learning experiences, improving knowledge retention, reducing administrative burdens and generally getting people excited about corporate training. 

Outside the confines of traditional academia, which is traditionally hesitant to adopt new technologies, workforce development is ripe for innovation. It provides a perfect arena for experimenting with new learning modalities. 

The Impact of Generative A.I. on Workforce Development

According to a recent McKinsey report, generative A.I. has three significant transformative advantages and the potential to transform workforce development programs in several ways:

• A.I. systems can improve knowledge retention and increase engagement via personalized learning experiences that generate content tailored to individual employees’ needs and learning styles.
• Generative A.I. can help employees practice and develop new skills in a safe and controlled environment by simulating real-world scenarios experienced on the job. This is useful for industries that require hands-on training, such as healthcare and manufacturing. However, it also creates an entirely new opportunity for knowledge workers to rehearse and refine critical human interactions with artificial intelligence acting as an external/internal stakeholder. 
• Natural language processing can provide real-time feedback and assessment, reducing bias inherent to human intelligence and freeing instructors to focus on more strategic and creative tasks. This can help reduce the administrative burden of training and development and improve the overall efficiency of corporate learning programs. 

Examples of A.I. in Workforce Development

A growing number of companies use generative A.I. to enhance workforce development. IBM is using generative AI to create personalized learning experiences for its employees. The company’s A.I.-powered learning platform uses machine learning algorithms to generate content tailored to individual employees’ needs and learning styles. Though large enterprises are leading the charge with A.I.-driven innovation, this technology is not limited to large-scale global organizations. A.I. use is increasing among smaller organizations seeking to streamline and improve specific workflows, such as performance reviews and training sales teams to improve the customer experience.

By replacing human intelligence with chatbots in training initiatives that rely on real-time data analysis and feedback, companies can more objectively and efficiently measure performance. In certain use cases, employees can reenact routine tasks and typical job scenarios with virtual assistants and natural language processing. For example, A.I. tools can help human resources teams improve their delivery of difficult news like job losses, grading hard metrics like legal compliance and measuring soft skills like listening and exhibiting empathy. On the flip side, generative A.I. can train hiring managers on interviewing best practices, helping them push job seekers beyond regurgitating their resumes and into conversations that help managers assess whether interviewees have the required skills and experience. 

A.I. systems can even perform 360-degree reviews, delivering results that are more precise, objective and thorough. Automating data analysis also reduces administrative work for management and HR teams, streamlining the entire process.

The Future of Work With Generative A.I. 

As the use of generative A.I. in workforce development continues to grow, expect to see even more innovative applications. Across industries, generative A.I. will be increasingly used to create virtual reality training experiences or to develop A.I.-powered coaching and mentoring programs. Of course, challenges and risks are to be expected of any new technology. A.I.-generated content can be biased or inaccurate, and employees can become too reliant on A.I.-powered learning tools. To mitigate such risks, companies must develop clear strategies for using generative A.I. in workforce development and ensure that A.I.-powered learning tools are designed and implemented in transparent, fair and accountable ways.

The pace at which A.I. is evolving can seem daunting, almost as if we’re watching the rise of the internet all over again—each step forward rewriting the rules on how we work. The paradox of AI accelerating a rapidly growing skill gap while being part of the solution is fascinating. As a result, corporate development will play an increasingly vital role in ensuring organizations stay agile and capitalize on competitive advantages made possible by this technology. As with any other experimentation process, it’s critical to implement systems in place to objectively measure the impact of how an investment, regardless of its modality, enables that advantage. Herein lies the most powerful and enduring application of A.I. in workforce development.