How Public Sector Leaders are Starting to Engage with AI
AI is no longer hypothetical. AI is woven into the systems and tools that public sector organizations rely on—sometimes visibly, but often without much notice.
This second post in BRONNER’s AI series looks at how government agencies have begun to engage with AI in practice: where AI is showing up, what use cases are gaining traction, and how leaders can take measured steps forward.
From Curiosity to Capability
Interest in AI is growing in finance departments, community development departments, HR offices, across cities and counties, transit providers, and public housing authorities. Many leaders are in learning mode: tracking how vendors integrate AI features, exploring pilot use cases, and evaluating where AI fits into their broader strategy.
In the meantime, staff are experimenting. Teams are using AI to summarize meeting notes, draft communications, or extract insights from lengthy reports. These efforts may seem small, but they mark a shift. AI is entering workflows informally, often without a defined strategy or risk framework.
The question now isn’t if AI will affect government—it’s how it’s being used, and whether leaders are prepared to guide its adoption.
Use Cases Emerging in Practice
Public sector AI use is still maturing, but some clear themes are taking shape:
Process Automation: AI is speeding up tasks like document review, routing, and contract management, especially in benefits administration or compliance-heavy areas.
Data Review and Monitoring: Machine learning helps surface trends and outliers in large datasets, supporting functions like grant management and financial oversight.
Staff Productivity: Generative AI tools assist with summarizing content, drafting emails, or tagging documents—freeing up staff for more strategic work.
Forecasting and Planning: Agencies are exploring how AI might analyze historic demand patterns to improve resource allocation, particularly in housing and social services.
These use cases offer a mix of benefits: faster cycle times, stronger compliance, and improved service quality.
Incremental vs. Transformational Impact
AI doesn’t need to deliver radical change on day one. For most agencies, initial wins will be modest: speeding up reviews, simplifying paperwork, or improving internal workflows. Over time, deeper value may come from rethinking entire service delivery models.
Getting both types of value—incremental and transformational—requires laying the groundwork now: building data maturity, modernizing outdated processes, and preparing staff for new ways of working.
Staying Grounded While Moving Forward
Certain applications—like predictive maintenance, eligibility screening, or public safety—demand special caution. Risks of bias, lack of transparency, and unclear accountability are real. But they are not reasons to avoid AI altogether, they are reasons to proceed with intention.
That means asking hard questions of vendors, clarifying internal responsibilities, and crafting governance structures before higher stakes use cases take root. BRONNER can support agencies thinking through these tough decisions with structured approaches for implementation.
Up Next
In the final post, BRONNER will provide a practical roadmap for adopting AI responsibly in the public sector—starting with what’s already in use, and building toward stronger oversight, smarter procurement, and informed experimentation.