AI in Government Grants: Finding the Right Workflow

Artificial intelligence is rapidly becoming part of how cities and counties approach grants. Local government teams are experimenting with tools like ChatGPT, AI-powered search, and document summarization to move faster, reduce manual work, and keep pace with a growing volume of funding opportunities. At the same time, grant-native platforms are applying AI directly to the grant lifecycle itself.

What's missing is a clear, practical view of how AI actually fits into government grant workflows today.

This series is designed to provide that clarity. Rather than focusing on policy debates or theoretical future uses, the posts that follow break down how AI is being used right now across the grant process, where free tools help, and where grant-native systems fundamentally change what's possible for local governments.

Why Interest in AI for Grants Has Accelerated

Federal infrastructure, climate, and economic recovery funding has dramatically increased the number of competitive grant opportunities available to cities and counties. Staffing levels, however, have largely stayed the same.

Many local governments are managing grants with:

• Small or part-time grant teams
• Increasingly complex eligibility and application requirements
• Tight timelines and overlapping funding cycles

AI has emerged as a way to absorb some of that pressure. For many teams, it offers the first real opportunity to reduce time spent searching, summarizing, and organizing information, allowing staff to focus more on strategy and execution.

How Local Governments Are Using Free AI Tools Today

Free and general-purpose AI tools are already embedded in many grant workflows. Cities and counties commonly use them to:

• Brainstorm potential funding opportunities based on project ideas
• Summarize long funding notices and agency guidance
• Translate technical language into plain English for internal review
• Quickly research program goals or funder priorities

These tools are especially useful early in the process. They help teams move faster during discovery and initial review, particularly when capacity is limited. For many local governments, free AI tools are the first step into AI-enabled grant work.

Where Free Tools Help and Where Workflow Breaks Down

Free AI tools provide speed and accessibility. They lower the barrier to getting started and make large volumes of information easier to process.

What they do not do is manage the grant workflow itself. They don't:

• Determine eligibility for a specific jurisdiction
• Retain institutional knowledge across applications
• Track requirements, deadlines, or ownership
• Connect discovery, review, research, drafting, and coordination
• Provide a shared system for teams working together

As a result, insights generated by free tools must be manually validated, re-entered, and coordinated across spreadsheets, documents, and email threads. The tools are helpful, but the workflow remains fragmented.

This isn't a limitation of AI capability. It reflects the reality that grants are not a single task. They are a multi-stage process that depends on continuity and structure.

What Grant-Native AI Looks Like in Practice

Grant-native AI applies intelligence inside a system designed specifically for how grants work.

Instead of generating isolated outputs, it supports the workflow end to end by:

• Assessing eligibility before teams invest time in deep review
• Turning NOFOs into structured requirements rather than static PDFs
• Grounding research and drafting in local documents and public data
• Enforcing character limits, word counts, and required sections while writing
• Coordinating tasks, timelines, and ownership across teams
• Preserving decisions, interpretations, and context across applications

This is the approach Avila was built around.

Avila applies AI across the pre-award grant lifecycle, connecting discovery, eligibility, NOFO simplification, research, drafting, and coordination in a single workflow. Rather than treating AI as a standalone assistant, it embeds AI into the system that manages grant work, so context carries forward and requirements are enforced as work happens.

For local governments, this shift matters. It reduces rework, lowers risk, and makes it possible to pursue more funding opportunities without adding staff or complexity.

How This Series Approaches AI in Government Grants

Each post in this series walks through a specific stage of the grant process the way cities and counties actually experience it.

At each stage, we look at:

• How free or general-purpose AI tools are being used today
• Where grant-native software changes what's possible
• How a connected system improves outcomes for local governments

The posts that follow cover:

• Grant discovery with AI
• Eligibility assessment before NOFO review
• NOFO simplification and structured requirements
• Grant research and drafting grounded in local and public data
• Grant management and coordination through submission and early post-award planning

Taken together, the goal is to show how AI, when applied inside a system designed for grants, helps communities pursue and secure more funding without increasing operational burden.