The government’s highest priority investment in artificial intelligence needs to be its AI workforce. It is not adopting AI as quickly as the private sector, and potentially as quickly as our adversaries. Most government teams developing AI solutions we have met face high barriers when they begin a project. They include limited access to data sets, constrained system authorities, and less computing power than they need. As a result, projects are slower and more expensive than they might be, delaying the fielding of systems that can decrease costs, increase capabilities, and help improve national security.
An educated, trained, and empowered AI workforce can act as a catalyst, enabling the government to create and adopt AI capabilities far more quickly and effectively than it does now. If a workforce can manage data, purchase and maintain compute; if domain knowledge and AI experts can work together, then it will create and adopt AI capabilities more quickly and more effectively. Just as importantly, a well-trained workforce will better understand when and how to purchase commercial solutions for immediate implementation, when to adapt commercial solutions to organizational needs, and when to develop custom software. Other priorities, such as internal projects, acquisition and contracting reform, and improving public-private partnerships will all improve faster and more effectively with an AI literate workforce.
Building an AI Workforce
The government needs to fix the hiring process for AI practitioners. AI practitioners that want to join the government often face long hiring processes, slow security clearance reviews, and human resource teams that struggle to read resumes filled with niche knowledge. As a result, world-class experts have been told they are under qualified to work in the government, or can only enter as a GS-5.
Support staff play an important role, and often don’t receive the training and education they need. Even if the government puts a great deal of effort into developing a technical workforce, it will struggle if it does not have the lawyers, contracting officers, acquisition professionals, and human resource teams it needs. People in support roles help technical experts join the government, purchase capabilities, write data sharing agreements with the private sector, and build ethical frameworks. If they do not understand AI, the rest of the government’s AI efforts will struggle, and likely fail.
The government should also develop its own technical experts. While the government can purchase some AI capabilities, it needs its own technical experts to implement and update commercial solutions, and when necessary, to develop AI internally. To fill this need, the government should establish the ability to train its own technical workforce, just as it does with medical professionals, lawyers, and some engineers.
The last step we will mention is senior leader education. Senior leaders who do not directly participate in the development process still play an important role in the AI workforce. They create their organizations’ goals, allocate resources, make personnel policies, manage careers, and set the standards for how their subordinates will use AI-empowered systems. If they do not have the education or experience to make well-informed decisions, their organizations will suffer as a result. Organizations can educate their senior leaders by creating short courses, including artificial intelligence in continuing education requirements, ensuring internally developed leaders have opportunities to serve in emerging tech roles, and by valuing technical experience during the leader selection process.
The government is serious about discussing AI. It’s time to figure out if it’s also serious about adopting AI. If that is the case, the government needs to invest in its workforce.
Mignon Clyburn is a National Security Commission on Artificial Intelligence (NSCAI) commissioner and Justin Lynch is research director at NSCAI.