Building AI Readiness in Grants Management: Where to Start

For governments and nonprofits managing grants, the "capacity crunch" is a persistent challenge: more mission-driven work than time to do it. As funding uncertainty, workforce pressures, and administrative demands intensify, organizations are exploring how artificial intelligence (AI) can support grants management operations.

While large institutions may be moving quickly, smaller governments and nonprofits are often earlier in the AI journey. For these organizations, the question is no longer whether AI will reshape grants management, but how to begin in a way that is strategic, secure, and practical.

To close that gap, successful early adopters start small, build internal readiness, and focus on high-value use cases that directly support the grants lifecycle. This is the first step in that process: getting your data, processes, and people ready before you adopt a tool.

Key Takeaways

• Organizations should inventory and standardize grants data, including applications, budgets, reports, and subrecipient files, before adopting AI tools, since AI performance depends on data quality
• By documenting processes, organizing data, piloting supportive use cases, and building staff confidence, organizations can prepare for a future where AI is a routine part of grants management
• Low-risk pilots, such as drafting grant narratives or summarizing funding announcements, help staff build confidence with AI without replacing human judgment
• Basic AI governance, including a written policy and short staff training, reduces hesitation and the risk of sensitive information ending up in unsecured tools

Where Should Grant Offices Start With AI?

One critical early step is establishing a clear starting point, or baseline. Many organizations underestimate the importance of data hygiene, but the success of any AI system depends on the quality, structure, and accessibility of the underlying data.

Before implementing tools, recipients should inventory their grants-related data (applications, budgets, quarterly reports, procurement records, subrecipient files) and assess where information is inconsistent, siloed, or stored in formats that AI tools cannot easily interpret. Even modest efforts to standardize naming conventions, consolidate storage locations, or digitize paper-based records can dramatically improve future AI performance.

Planning Your AI Integration Activities

Integrating AI is not about replacing human knowledge or making massive financial investments; it is about creating a Human-in-the-Loop framework. By focusing on small, manageable steps, organizations can reduce the time spent on repetitive administrative tasks while preparing for broader implementation milestones.

Specific action steps can represent the first stage of AI adoption, focused on establishing readiness through data, process, and governance improvements before gradually scaling into advanced capabilities.

Reflect on these two questions when considering AI implementation:

1. If your team were to adopt an AI tool tomorrow, would your data be organized enough for it to be useful?
2. Do you have a clear picture of where your staff spends significant time on repetitive, manual tasks across the grants lifecycle?

Strategies to Implement AI in Grants Management

AI Governance

Before adopting new tools, it is helpful to establish basic environmental guardrails. This often involves developing both policy and basic competency.

• "AI Policy" defines acceptable use within the organization, outlining how employees can use AI safely and responsibly. At a minimum, the policy should address which tools are authorized, what types of data may and may not be entered into AI systems, how AI-generated outputs must be reviewed before use, and how compliance will be monitored. Organizations that establish these guardrails early tend to adopt AI more effectively because staff have clear guidance on appropriate use, reducing both hesitation and the risk of sensitive information being shared in unsecured tools.
• Foundational AI familiarity should complement these policies. Organizations should pair them with basic staff training. Even short, practical sessions can help staff better understand how to use AI tools appropriately within the grants lifecycle.

Process Mapping and Optimization

Many teams rely on workflows that have evolved organically over time. Before introducing any AI tool, it is helpful to outline how applications are developed, how subrecipient information is collected, how expenditures are tracked, and how reports are produced. This does not require specialized software; a simple flowchart or checklist is sufficient.

The goal is to identify repetitive tasks, bottlenecks, and areas where staff spend significant time, because these are precisely the areas where AI delivers the fastest returns. When the grants lifecycle is clearly mapped, it becomes evident where AI can accelerate application development. Key benefits may include reusing and adapting prior submission content, reducing report preparation time by consolidating data from disparate sources, or strengthening compliance checks well before submission deadlines.

Data Readiness and Governance

Organizations should review their data readiness. AI tools depend on accessible, well-organized information. Grant files are often stored across shared drives, email inboxes, and paper folders. Consolidating documents, standardizing file names, and storing key records in consistent locations can make future AI tools more effective. Even modest improvements in data organization can strengthen the foundation for AI-enabled processes.

The question often asked is, "Where do we start?" The answer is to prioritize the data AI will use soonest: past performance reports, successful application narratives, budget templates, and subrecipient records. These are the documents that yield immediate value when paired with AI summarization, drafting, and analysis capabilities.

Organizations may also wish to refine basic data governance practices. Governance does not require a formal committee or new systems. Organizations can begin by identifying what information is sensitive, who should have access to it, and how it should be stored. This is especially important for grant recipients handling personally identifiable information, financial data, or research-related materials. Many AI vendors now emphasize a privacy-first design, but internal guidelines remain essential to confirm that staff do not inadvertently upload sensitive information into public tools.

A practical distinction that strengthens both governance and staff confidence is to clearly separate data intended for AI consumption (NOFOs, guidance documents, draft narratives) from data that must remain protected (PII, Social Security numbers, financial account details). Linking this distinction directly to the organization's AI Policy reinforces the guardrails and gives staff a clear, repeatable decision framework.

Early Pilots and Practical Experimentation

Piloting AI within low-risk, supportive tasks is a strategic way to plan for early implementation. Early pilots should focus on activities that assist staff without replacing judgment or decision-making. Examples include:

• Generating first drafts of grant narratives
• Summarizing lengthy funding announcements
• Creating checklists from program guidance
• Drafting internal communications

These use cases are easy to test and align with recommendations from public-sector AI practitioners. They also help teams build comfort with AI while maintaining full control over compliance-related outputs. To that end, some organizations may wish to build staff familiarity through guided experimentation.

Many modern AI tools are designed for non-technical users, and hands-on practice is often the most effective way to build confidence. Organizations can encourage staff to try sample prompts tailored to grants management, such as drafting a budget justification or summarizing a funding announcement, while reinforcing that human review remains essential. Short, peer-led learning sessions can help teams explore AI capabilities in a structured environment.

Moving Forward with EisnerAmper

With this foundation in place, organizations are ready for the next question: how to scale AI-generated work without compromising compliance or security.

At EisnerAmper, our team helps organizations adopt AI for long-term success, without compromising compliance, security, or scalability. To start your modernization journey, contact us today.