Two topics almost always come up whenever I talk to talent management professionals — upskilling and reskilling the workforce and generative AI (GenAI).
But they rarely come up in relationship to each other, although they should.
Most organizations believe GenAI will enhance and scale training activities (60%) and accelerate training content development (57%), according to the Brandon Hall Group™ study, How GenAI Will Revolutionize HR. Far fewer (21%) see a use case related to job skills identification.
TalentGuard, a Brandon Hall Group™ Smartchoice® Preferred Provider, is ahead of the curve here. Their WorkforceGPT product, launched earlier this year, can generate a nuanced taxonomy that accurately captures the skills that are most relevant to a particular role.
Why is that important? The great majority of employers surveyed (87%) are adding, or plan to add, some type of new skills requirements for up to one-half of all existing job roles, according to a Brandon Hall Group™ Study on upskilling and reskilling the workforce.
However, 35% of organizations say they are highly challenged to identify the specific skills needed for job roles. How can you communicate career paths and develop employees to fill roles when you don’t really understand which skills each role needs?
This is where GenAI can be a game changer. When applied to the problem of building a skills taxonomy, generative AI models can be trained on a vast array of data sources such as skill frameworks, labor market data and personal career profiles like LinkedIn.
By analyzing these diverse data sources, generative AI models such as WorkforceGPT can produce a taxonomy that accurately captures the skills that are most relevant to a particular role. Furthermore, GenAI can complete tasks exponentially faster than humans can. With generative AI, skill taxonomies can be updated with the latest data in near real-time.
This sounds great — and it can be — but employers need to educate themselves before they can fully reap the benefits. A striking 70% of organizations surveyed by Brandon Hall Group™ say they do not have the right skill set to ensure that Gen AI-developed content is accurate, reliable, or legally sound. And doubts about GenAI abound:
To ensure that the taxonomy generated by a generative AI model is accurate, you should know the steps to take so you can trust the skills data you will use.
TalentGuard, which began developing its knowledge long before most providers became interested, has done a good job of outlining the steps here, including model tuning and data pre-processing.
