The Department of Housing and Urban Development (HUD) is reevaluating the way it identifies and addresses skills gaps within its workforce, with a specific focus on competencies in Artificial Intelligence (AI). Rather than relying on traditional methods of self-evaluation, HUD has launched a unique skills competency model aimed at precisely identifying skills gaps in AI and devising strategies to address them.
Central to this new approach is moving away from an outdated method of skills assessments that depend heavily on employees’ self-evaluations. While this traditional model has its advantages, it tends to create scenarios where employees either exaggerate or underestimate their competencies. This situation is especially significant given the relative newness of AI as an area of skill.
Notably, the “old school” model may hinder the accurate assessment of employees’ skills in new and emerging areas like AI. This is particularly true because an employee’s immediate supervisor, who can generally perform a good assessment, cannot accurately gauge the employee’s competence in a rapidly changing and relatively new area like AI.
HUD is adopting a “skills benchmark model” to overcome these challenges. This new model focuses on taking a more active test-based approach to measure employees’ competencies and knowledge levels in different aspects of AI. The goal is to assess technical and non-technical employees’ understanding levels of AI and its practical applications in the workplace.
HUD’s commitment to creating an AI skills competency model aligns with prevailing government directives to develop strategies to manage the risks associated with the use of AI. Simultaneously, it serves as an investment in the capacity development of federal employees to ensure there are sufficient skills and knowledge about AI within the workforce.
One of HUD’s critical approaches is its effort to categorize employees based on their AI skill levels. Using the results from the AI skills assessment, employees will be assigned places according to their current understanding of AI. This will facilitate the development of personalized training plans aimed at improving their AI skills relevant to their everyday work.
The primary aim of this new competency assessment model is to tailor learning to each employee’s specific needs. The approach also serves to anticipate and address resistance to the new AI competency model. Since many of the department’s employees are not technologists by profession, it is expected that some resistance might occur.
HUD believes addressing this resistance is as crucial as the skills development itself. This involves communicating the importance of the training, connecting AI to everyday work, and relieving any concerns employees may have towards AI.
The bigger picture is that HUD seeks to show employees the critical role AI can play in enhancing efficiency and improving their job performance. Ultimately, the goal is not to lay off employees. Instead, it is about leveraging AI to help employees perform their jobs better and faster, providing an overall boost to productivity and service delivery. This effort marks a significant development in how public sector agencies identify their skills gaps and develop strategies to fill them. This innovative model, if successful, could inform similar efforts within the broader federal workforce.
