×
How state officials think about implementing and managing generative AI
Written by
Published on
Join our daily newsletter for breaking news, product launches and deals, research breakdowns, and other industry-leading AI coverage
Join Now

Generative AI in state governments: A team approach: NASCIO President Jim Weaver advocates for a collaborative strategy in managing generative AI implementation, rather than relying on a single chief AI officer.

  • At the NASCIO 2024 Annual Conference, Weaver expressed skepticism about the need for chief AI officers, viewing them as potentially adding unnecessary bureaucracy.
  • Weaver, who is also the North Carolina CIO, suggested that a team-based approach would be more effective in driving value and managing the complexities of generative AI adoption.

Creative applications and challenges: State officials at the conference explored innovative uses for generative AI while acknowledging the complexities involved in its implementation.

  • Weaver proposed using generative AI for legislative code analysis, helping IT departments identify relevant clauses in seemingly unrelated bills.
  • Missouri Deputy CIO Paula Peters highlighted the challenges of implementing chatbots, emphasizing the need for thorough preparation, including indexing millions of documents and rigorous training and vetting.
  • The importance of getting AI implementations right the first time was stressed, as public trust can be easily shaken by inaccurate AI-generated responses.

Budgetary considerations: States must carefully evaluate the long-term financial implications of adopting AI technologies.

  • Beyond initial purchase costs, states need to factor in ongoing maintenance expenses for AI systems and solutions.
  • Projecting per-month costs for AI use cases can be challenging, requiring careful planning and budgeting.
  • Indirect costs, such as potential increases in public records requests facilitated by AI, may necessitate additional staffing and resources.

Infrastructure challenges: On-premise AI implementations present unique infrastructure demands that states must address.

  • Generative AI systems require significant electrical power, potentially straining local power grids.
  • Water for cooling AI systems is another critical consideration, with Weaver noting that many data centers are not equipped for this requirement.

Vendor selection and transparency: Nebraska CIO Matt McCarville emphasized the importance of transparency in AI procurement for government agencies.

  • Regardless of pricing or discounts, governments cannot afford to purchase AI systems without understanding their inner workings.
  • CIOs need to comprehend algorithm functionality, potential biases, and error rates, especially when handling public data.

Broader implications for state governance: The adoption of generative AI in state governments raises important questions about resource allocation, infrastructure readiness, and public trust.

  • States must balance the potential benefits of AI with the need for responsible implementation and management.
  • The team-based approach advocated by Weaver suggests a shift towards more collaborative and interdisciplinary AI governance in state governments.
  • As AI technology continues to evolve, state officials will need to remain adaptable and vigilant in addressing new challenges and opportunities in public sector AI adoption.
NASCIO President: Tackling Generative AI Takes a Team

Recent News

Social network Bluesky says it won’t train AI on user posts

As social media platforms debate AI training practices, Bluesky stakes out a pro-creator stance by pledging not to use user content for generative AI.

New research explores how cutting-edge AI may advance quantum computing

AI is being leveraged to address key challenges in quantum computing, from hardware design to error correction.

Navigating the ethical minefield of AI-powered customer segmentation

AI-driven customer segmentation provides deeper insights into consumer behavior, but raises concerns about privacy and potential bias.