This Insights post is a part of a series about the use and adoption of AI within the government. Read the other Insights posts here: “Yes. We do need Chief AI Officers“ and “Managing for AI in the Federal Workforce“.
The excitement of artificial intelligence (AI) isn’t going anywhere soon. With the explosion of AI language models being applied to business needs, the extraordinary demand for technically skilled professionals is unlikely to show signs of abating anytime soon. A recent analysis of job postings by the University of Maryland and LinkUp shows that the majority of these AI jobs are located in the Bay Area (unsurprising given the centralization of technology companies), but that Washington, D.C. is not far behind. This finding may be driven by the federal government’s push to quickly attract and hire AI-capable staff to advance the processes that run the agencies and departments (and, in some cases, stay one step ahead of the public).
Most recently, the Department of Homeland Security (DHS) announced an ambitious target of hiring fifty new federal employees to support AI advancement in alignment to the current administration’s emphasis on increasing the capability across the government (Learn more about DHS’s AI use cases here.) This is an impressive goal given the competition over talent and the lag seen by the government in hiring data scientists, a role that is related but not completely the same. The likely solution to hiring AI talent, much like data scientists, is not anything groundbreaking but certainly remains a hurdle that the government must overcome to achieve these recent goals.
Lessons from past tech booms have a lot to offer in addressing this challenge. Hiring and integration of new, technical staff – and the ability for the staff and organization to align & grow together – will come down to securing the right talent for the right challenges at the right moment…
- Data Readiness – There are multiple pieces to this component. Part of it is having the right infrastructure, such as the right software or cloud environments to build and deploy AI tools. Part of it is having good quality data, arguably the most important piece to building AI models. Organizations should hire their CAIOs, build a roadmap, and prepare their infrastructure and data. AI talent will not want to spend their time building data pipelines, cleaning data, or facing uncertainty around where to build applications.
- Highlight Unique Problem Set – This isn’t just about serving the mission. This is about being immersed in unique and complex datasets that, in some cases, have not yet been diluted or further complicated by integration with non-relevant third-party data.
- Emphasize the Ethical Vision – For AI professionals, grasping the ethical implications and the intended application of AI within the mission space they are joining is essential. It is important to maintain transparency among the team regarding the use of AI, data management practices, and the mission’s dedication to ethical AI, which is becoming a key concern for AI experts.
- Showcase the Impact – Now, this is where it is important to talk about the mission. An incredibly important mission that has deep and profound influence on both our country’s efficiency and effectiveness toward its citizens and in some cases protecting the homeland from a wide variety of threats will be a real appeal for some candidates.
- Modernize Hiring & Compensation – As the largest employer in the country, the federal government’s hiring and human resource processes are too often mentioned in a joke’s punchline. To overcome talent gaps, the government will need to take a serious look at everything in talent management from recruitment, the clearance and compensation process, and even performance management and continuous learning.
Although the federal government faces a steep challenge, these lessons from past tech booms provide a hopeful outlook into necessary action steps to achieve the essential maturation of capabilities within artificial intelligence.
