When agencies try to tackle skills gaps in their workforces, they may typically ask both supervisors and employees to evaluate their skills to determine what training might be needed — but the Department of Housing and Urban Development is looking to transform that approach.

Instead of viewing a skills gap as the space between how a supervisor rates an employee’s skills, and how that employee rates their own skills, HUD launched a new skills competency model to determine more accurately where the skills gaps in AI exist — and then, work to fill in those gaps.

“We have absolutely been focused on identifying where our skill gaps are, because unfortunately, we can’t really do much about it if we haven’t identified it,” Matisha Montgomery, chief learning officer at HUD, said during a panel at an Oct. 9 event hosted by software company Cornerstone. “What we’re trying to do is really shift from a very historic old school model around skills assessment and utilize emerging technology to help us do it better.”

For Montgomery, the “old school” model of skills assessments relies on self-evaluations, which in turn can cause employees to either overvalue or undervalue their competencies. Creating a new competency model for AI in particular may be especially important because of how new the topic is.

“The supervisor may or may not actually know where the employee truly is on that new and emerging skill,” Montgomery told Federal News Network after the panel. “If they see it every day, a supervisor is usually a good rater or assessor for that skill. But AI isn’t something that everybody’s doing. So right now, having your supervisor say where your skills or knowledge level is on AI — nobody could do that.”

HUD’s shift to a “skills benchmark model” relies instead on a test meant to measure individual employees’ competencies and knowledge levels of various AI topics. An optional skills assessment sent to HUD employees last week aims to evaluate individuals’ various levels of understanding of AI and how it applies in the workplace — not only for technologists, but across the agency’s entire workforce.

“This is a nascent area that is emerging, and quickly and rapidly changing as well,” Montgomery said. “Right now, we’re expecting that our workforce, who is not made up of a ton of technologists at HUD, is really going to be at a low level.”

The AI skills competency model at HUD stems from governmentwide requirements of the March 2024 AI memo from the Office of Management and Budget that asked all agencies to develop strategies to mitigate the risks of using AI, while also in part managing and developing federal employees’ skills in AI. For the federal workforce, a skills gap arises when there isn’t enough institutional knowledge among employees — or if there aren’t enough employees in the first place — who understand a certain work topic, such as AI.

Montgomery plans to collect the data and use the results of the AI skills assessment to place HUD employees in various categories of skill level. Based on employees’ starting point in their understanding of AI, they’ll be able to use a personalized training plan to help them develop their AI skills, tailored to what they might need to know for their day-to-day job.

“We want it to be something that’s personal for them and a journey that they’re about to embark on,” Montgomery said. “Not only will they have the opportunity to find out where their skills lie, but then based on the results of their assessment, they’re going to get personalized recommendations on what they need to take to actually help them improve.”

Since many of HUD’s employees aren’t technologists by trade, Montgomery said she’s expecting some level of resistance toward the new AI competency model.

“It’s a change, and even if we didn’t do a skills assessment and we were just implementing AI technologies or tools, there would be resistance as well,” Montgomery said. “The reason resistance happens, a lot of time, is because of lack of understanding. That’s exactly the space we’re living in.”

But to help employees through the changes, HUD plans to communicate why the training and skills development is important, and how AI relates to employees’ hands-on, everyday work. Addressing the expected resistance will also involve allaying concerns — or even fears — from employees around AI.

“We want to make sure that no matter what position you’re in, what role you have at HUD, you can see the value that AI can play in helping you do your job more efficiently,” Montgomery said. “Not to reduce the need for your job, but to do your job exponentially better or faster.”

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