AI Demands A New Kind Of In-The-Job Learning

Written by
Allison Salisbury
Published on
9.12.2023
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AI has taken Wall Street and the American imagination by storm. And while its impact on everyday work life is still mostly speculative, millions of workers are understandably nervous.

Six in 10 believe they’ll need to learn new skills as a result of AI, but the vast majority don’t trust their employer to support them in understanding the technology. This emerging skills gap comes on top of existing challenges—with employers across industries, but especially in healthcare, transportation, and certain tech fields, already struggling to find and keep qualified workers.

For employers, this should be a wake-up call. They’re going to need to step up their strategy and skilling investments in a big way to account for how AI is going to exacerbate an already turbulent talent market.

In a recent issue of the Harvard Business Review, a set of researchers argue that the coming disruptions look to be so significant that upskilling alone won’t be enough: “Millions of workers may need to be entirely reskilled—a fundamental and profoundly complex societal challenge that will require workers not only to acquire new skills but to use them to change occupations.”

Much of this has to do with how AI will combine with other forms of automation, like robotics and software advancements. The current expectation is that generative AI will predominantly be used to work alongside people, not to replace jobs wholesale. Half of companies worldwide expect generative AI to create more jobs than it destroys, while only a quarter expect to see a net loss. But even so, McKinsey Global Institute estimates that generative AI will lead to a 36% increase in task automation over what would otherwise have been expected—from 22% of today’s work hours to 30%.

How exactly this plays out certainly remains to be seen—but it’s clear that employers need to be making new education and skilling investments now. Three foundational elements should guide that work:

1. A focus on AI literacy will be required to prevent further workforce equity gaps.

If you believe that generative AI will predominantly work alongside people, then people are going to need to know how to work alongside AI. Across all roles, developing AI literacy will need to be a major focus for employers—especially if they want to prevent a widening of already large equity gaps.

Guild’s research on its employee members has shown that those who identify as men, who are more highly educated, have higher household incomes, and are white are more likely to gain further advantages from increased AI tool usage. They are more likely than other workers to report that AI tools help them perform better in their current roles, that they are aware that they will need to learn new skills to position themselves to remain competitive, and they have some understanding of how these tools can help them navigate the job market. And in our market scans, at least half of the education programs focused on AI require at least a bachelor’s degree to enroll (I’ll have more to say on that in a forthcoming piece).

All that taken together, it’s clear that equity will suffer if workers are left to figure out the shifting landscape on their own. We’ve already seen that play out with digital literacy—and an intentional focus on raising AI literacy across the board will be critical. As it stands now, very few of the learning programs in the market are targeted at frontline workers, or even non-tech workers. That needs to change, and fast.

2. The most future-proof skills of all won’t be technical skills, but the ability to learn and adapt.

Employers will need to focus on helping more workers develop the soft skills or human skills, like creativity, problem solving, and advanced communication, that are valuable on the job and aren’t the kinds of routine tasks and production that can be done by AI. Attitudes like flexibility and agility and an interest in continual learning may become the most future-proof skills of all.

George Siemens, a pioneer in digital learning, has long argued that entrepreneurs and education reformers are putting far too much energy into creating adaptive learning technology when what we should be doing is finding ways to create more adaptive learners. “To make a process more efficient that shouldn’t be done at all is a waste of time,” he told EdSurge years before ChatGPT made its public debut. And yet, many of today’s edtech solutions and sub-baccalaureate programs, especially for frontline workers, are still focused on helping learners develop routine skills.

You shouldn’t have to spend a semester reading Chaucer or get a four-year liberal arts degree in order to develop analysis and critical thinking skills. Those things can be just as easily taught in a one-year technical program—it just has to be designed to do so. That means doing project-based learning and assessing how learners apply technical knowledge in complex scenarios, rather than focusing on rote memorization and basic technical mastery. Across the board, education programs are going to need to set a much higher bar for the durable skills they’re imparting no matter what the field or subject matter.

3. Workers need to be in the driver’s seat.

Individuals should be given choice in navigating this new reality. For employers, that means raising awareness of the potential impacts of AI—both displacement and new career opportunities—and ensuring workers have access to the education, training, and networks they need to make informed decisions and to shift in their careers.

This means doubling down on a skills garden approach that provides workers with quality, curated options for education and training, rather than an assembly line approach that demands that all workers in a given role pursue the same kind of upskilling or reskilling. Above all, that requires treating employees with respect, and being as clear as possible about the concrete career benefits of different kinds of education and training.

The HBR researchers, for example, cited research by BCG showing that 68% of workers are aware of coming disruptions in their fields and are willing to reskill. But workers often don’t participate in reskilling because the benefits of their participation aren’t made clear and they aren’t part of the design process. As one interviewee told the researchers: “The secret to scaling up reskilling programs is to design a product your employees actually like.”

While much is uncertain about AI’s impact in the coming years, the need for adaptability and continuous learning is clear. Employers can accomplish a lot if they center their upskilling and reskilling efforts on these three pillars—advancing AI literacy for equity, developing durable skills, and putting employees in the lead.

That focus may not accomplish everything that’s needed, but any approach that doesn’t include them is sure to collapse.

Written by
Allison Salisbury
Published on
9.12.2023
Url copied!

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