February 22, 2024
Generative AI brings major opportunities alongside risks for local governments with limited resources. By studying early federal applications and governance, local leaders can pivot towards responsible innovation.

As generative AI tools become more advanced and accessible, local governments have an opportunity to harness these technologies to transform services and operations. Local leaders can learn critical lessons to craft pragmatic adoption roadmaps suited to local contexts by studying early-gen AI initiatives at the US federal government level.  

Key Learnings 

  • Start with non-critical pilot projects to build capacity while limiting risks.
  • Establish cross-departmental governance and oversight before scaling
  • Assess infrastructure and data readiness to smooth adoption 
  • Prepare staff at all levels to appropriately leverage gen AI
  • Focus initial applications on high-value resident services

a robot in a suit, standing in front of the Whitehouse, giving a talk

Pilot Projects Enable Safe Experimentation

Small-scale pilots allow local governments to build know-how while exploring emerging technologies like gen AI while limiting downside risks. Federal agencies have focused initial-generation AI applications on narrow use cases like streamlining call centre operations or enhancing specific types of citizen services. Local leaders can replicate this measured approach by earmarking resources for low-risk trials focused on high-priority resident services.  

Starting with tightly scoped gen AI chatbots for frequently requested information or simple transactional services, for example, allows testing capabilities without disrupting critical agencies. Takeaways from controlled pilots, including data quality, staff readiness and impact metrics, and inform wider-gen AI strategies. Given civic technology resource constraints, future large-scale gen AI adoption can leverage lessons from these bite-sized experiments that put residents first.

Governance and Oversight Mitigate Long-Term Risks   

A robust governance structure for gen AI deployment creates accountability, ensures responsible use, and builds staff and resident trust. Federally, new bodies provide standards around development, procurement, security, equity and other issues. Locally, agencies must also safeguard public interest while enabling innovation. 

Local general AI oversight committees should include agency heads, legal experts, IT leaders, data scientists, and resident advocates. By covering expertise gaps and institutional blind spots, inclusive governance provides comprehensive risk monitoring. It also aids in creating localised gen AI principles and operating procedures reflecting community priorities. Community feedback strengthens guardrails against potential data abuse, bias, and other harms. 

Proactive governance lets residents reap gen AI efficiencies while avoiding overreach. It also reassures the public sector that it can integrate cutting-edge automation securely and ethically.  

Infrastructure and Data Readiness Streamline Adoption

Sophisticated machine learning requires extensive data infrastructure that many local governments lack. However, most out-of-the-box gen AI applications have minimal technical prerequisites. Still, auditing the adequacy of existing assets prepares agencies to capture total value.

This survey covers current structured/unstructured data accessibility, pipeline security, storage scalability, legacy compatibility, and sharing protocols. Findings should inform requests for upgrades like consolidated data lakes. Modernising dated systems ahead of complex-gen AI adoption also prevents future bottlenecks.

Inter-departmental legal agreements may need modernising to tap AI’s cross-functional potential while respecting privacy. Overall readiness checks and targeted improvements prevent integration headaches without demanding wholesale modernisation out of reach for most. They also allow more resources for high-return staff skills training and citizen-centric gen AI applications.

A smiling robot with a chat bubble full of 1s and 0s hovering by its mouth, waving, as residents communicate with an AI chatbot for city services

Training and Retraining Staff for the AI-Powered Future

Gen AI’s spread through everyday office tools like email and word processors will profoundly shift government work. Leaders must champion capability building so the public sector can tap automation’s promise while protecting staff from disruption. 

Training for senior officials should strengthen general AI literacy and strategy-setting skills. Similar upskilling for technical teams creates in-house expertise beyond vendor support to customise deployments. Reskilling admin staff for value-added roles reduces the risk of redundancy. Goal-oriented learning pathways for each personnel group boost adoption and management quality.

Change management support during ongoing AI integration also smooths the transition. Internal mobility programs that redeploy displaced labour prevent skill erosion while showcasing a people-first approach. Governments gain cutting-edge capabilities and organisational resilience by intentionally developing and empowering public servants.

Lead with Citizen Service Transformation  

Demand for superior, seamless local government services continues growing. Early federal forays applied general AI to elevate constituent experiences through personalised interactions. Prioritising similar citizen-facing applications builds public trust in automation’s benefits.

Intuitive chatbot case workers, for instance, provide 24/7 support by integrating data across agencies. They supply hyper-customised answers, from land use permits to small business loans. Automated translation functionality also reduces accessibility barriers for non-English speakers. 

Sharing successful experiments’ social impact metrics and citizen stories also helps secure resources for further modernisation; as generative AI infiltrates consumer technology, local leaders can deploy it as a tool for empowerment rather than just efficiency. High-touch pilots putting people first provide a foundation for fully realising AI’s civic potential while avoiding tech-centric pitfalls.  

Conclusion

Local governments operate under resource constraints radically different from federal agencies, but early cautions provide transferable wisdom. Local leaders can nurture generative AI as an ally instead of an adversary by first targeting small service wins under careful oversight while intentionally developing digital dexterity across personnel. Continued community participation through the process also roants technology use in public values for generations.

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