The National Association of State Chief Information Officers (NASCIO), the Center for Digital Government (CDG), and IBM released a study today on artificial intelligence (AI) trends among state IT executives.
While implementation of AI and machine learning technologies may be slow-going, the report found that states are moving to implement AI and machine learning technologies to improve both behind-the-scenes operations and citizen-facing services.
“Although the survey shows that states are still nascent in their implementations of AI and machine learning, 55 percent are actively pursuing AI by staging proofs of concept or evaluating requirements and issuing requests for information,” the report says. “Another 32 percent of states have progressed to running AI in some production operations or staging pilot projects.”
The study is based on survey responses from 45 states including state CIOs and their deputies, chief technology officers, and selected agency heads, as well as in-depth interviews with multiple state CIOs.
NASCIO President and North Carolina Secretary and State CIO Eric Boyette drove home why states should start a more aggressive push to implement AI: workforce shortage and cybersecurity.
“Like most states, we continue to face a shortage, but have found that having AI resources such as chatbots allows employees to work more efficiently,” Boyette said about the state he leads. “AI has also allowed us to quickly analyze a large volume of security threats so that we can focus on the ones that need immediate attention. While AI is new for most states, I think it’s the direction that are all moving in.”
The study tackles the challenges related to AI deployment, including legacy IT infrastructure, cultural concerns, lack of necessary staff skills for AI, and organizational data silos. It also provides states looking to ramp up their focus on AI recommendations and best practices, including encourage states to:
- Develop a unified framework for AI adoption;
- Create multidisciplinary teams to address change management;
- Asses existing data and how it is managed;
- Modernize legacy infrastructures with “targeted technology investments;” and
- Initially choose AI pilots and projects where success is easily quantifiable.
