Organizations are rapidly implementing AI, but employees often feel left behind about how AI is changing their roles. While AI can help get work done faster, it won’t necessarily help produce better quality work if employees aren’t confident in their AI skills.
In fact, a recent survey from workforce analytics firm Visier reveals that 48% of employees report exaggerating their AI expertise at least some of the time, and 45% said they often feel pressure at work to use AI despite not feeling confident with the tools.
“The risk of underinvesting in training and upskilling is that the alternative can be much more expensive,” says Andrea Derler, principal researcher at Visier. “Layoffs, rehiring, and competing for top AI talent all come with significant direct and indirect costs. Investing in the workforce organizations already have can be an important part of making AI adoption sustainable.”
When the pace of AI implementation is rushed, however, employees often default to AI compliance, meaning they’ll use AI tools as they’re rolled out, even if they haven’t received the proper guidance or training from their organization to make the most of the investment. So for companies investing millions into an AI future, adds Derler, it’s critical to ensure employee participation is part of the bigger picture to avoid performative AI usage.
Make the most of AI investments with training
Derler acknowledges that AI adoption goals vary widely from company to company. Some will need stronger investment in upskilling and training, while others might need to invest in new resources, talent, or tools to help employees get comfortable with the new pace of work, and to source the right skills for an AI-driven workplace.
Either way, the early stages of AI adoption should involve a basic framework to assist in employee adoption, one that involves open transparency and foundational guidelines for how AI will be implemented moving forward. At bare minimum, employees should be briefed on concepts around general data security, how to spot false outputs from AI tools, and expectations around AI’s impact on their job roles.
“A few foundational skills and concepts every employee should understand include protecting sensitive data and uploading information with the greatest care, understanding the basic capabilities of approved tools available at work, and checking the work before sending AI-generated output to others,” says Derler. “Most importantly, employees need to take full responsibility for the output. The AI isn’t responsible, the person using it is.”
Avoid setting blanket goals as well, where everyone is measured simply by a certain percentage of AI usage per individual or team. This can overlook reality, she says, creating a false narrative around AI usage and adoption, and a focus on quantity rather than quality. Instead, adds Derler, leaders need to strike a balance between mandating adoption and encouraging employees to explore, discuss, and learn together where AI makes sense.
Embracing transparency around AI
When asked if they felt their organization had a clear plan for how AI affects employees and jobs, 59% in the Visier survey said they didn’t believe or were unsure that their company had a clear plan for AI implementation. And only 28% said they felt leaders in their organizations fully understand how employees use AI today, while 33% thought leaders understand how much training and support employees need.
Overall, employees are eager to embrace AI, but when asked what would boost confidence around using it at work, respondents first ranked AI training and upskilling opportunities as most important, then greater transparency from leadership, more opportunities for career growth and advancement, followed by better communication about how AI will impact employees.
Clarity from the top
Employees have made it clear they want training and transparency from leaders about how AI will affect workflows and processes. This is especially true when it comes to career paths, as AI stands to remove certain skills and expertise deemed irrelevant.
Workers know the future will look different in the wake of AI, but they want to understand exactly what that looks like so they can prepare and shift their own expectations and career goals. Part of keeping employees engaged is ensuring they’re invested in their own future at a company, but that’s difficult if the future is particularly unclear.
“Most organizations are still learning which parts of a workflow AI can take over or augment, so creating opportunities for experimentation and learning is an important step before setting formal adoption goals or metrics,” says Derler. “Many organizations, however, are still piecing together workforce data and don’t have a complete picture of their workforce, making it harder to understand where AI can create meaningful efficiencies.”