How AI Helped Reduce Grant Writing Time by 50% for NGOs

Securing grant funding is essential for many NGOs, yet the process is often slow and resource-intensive. Teams routinely spend weeks searching for suitable opportunities, adapting similar proposals for different funders, and managing complex submission requirements. This work frequently runs in parallel with program delivery, placing sustained pressure on limited staff capacity.

In recent years, some organizations have begun rethinking how grant work is structured. Rather than focusing solely on writing faster, they have introduced AI-supported workflows to reduce repetitive tasks such as grant discovery, early-stage drafting, and submission tracking. This shift has allowed teams to move through the grant process more efficiently while keeping strategic decisions and final review firmly human-led.

Challenges in the Traditional Grant Process

For many NGOs, grant applications are among the most time-intensive administrative tasks. Limited staff capacity and largely manual workflows slow the process and make it difficult to sustain a consistent funding pipeline. Over time, this constrains how many opportunities organizations can realistically pursue.

Three challenges appear most often.

1. Fragmented and Time-Intensive Research

Grant opportunities are scattered across multiple databases, donor portals, and government websites. Identifying relevant funding can take weeks, delaying applications and reducing responsiveness to time-sensitive calls.

2. Limited Staff Capacity

Most NGOs operate with small teams responsible for both fundraising and program delivery. Preparing detailed proposals alongside ongoing field work stretches capacity and forces difficult trade-offs between funding efforts and direct impact.

3. Missed or Delayed Opportunities

When time and attention are limited, some funding opportunities are identified too late or not at all. This reduces application volume and can affect long-term funding stability.

Together, these constraints make grant work less predictable and more reactive, limiting an organization’s ability to plan and scale programs sustainably.

How AI Is Applied Within Grant Workflows

Rather than addressing grant challenges through incremental improvements in writing speed, some NGOs have restructured how grant work is organized. This shift reflects a broader move toward building and implementing AI products that are integrated into real operational workflows rather than used as standalone tools. AI has been introduced selectively to support repetitive and time-intensive stages of the process, particularly in grant discovery, early drafting, and submission management.

The changes are best understood at the workflow level.

1. Improving Grant Discovery and Relevance Matching

Instead of relying on manual searches across multiple platforms, AI-supported systems scan grant databases, donor portals, and public funding sources in parallel. Opportunities are filtered using predefined criteria such as focus area, geography, and eligibility, allowing teams to surface relevant grants more quickly and with less manual effort. Much of this capability depends on natural language processing (NLP) to extract, classify, and interpret unstructured text from grant databases, PDFs, and donor portals at scale.

This reduces time spent on preliminary research and lowers the risk of missing suitable opportunities due to capacity constraints.

2. Accelerating Early-Stage Proposal Drafting

AI is used to support the creation of structured first drafts based on project inputs and funder requirements. This includes outlining project descriptions, aligning objectives with donor criteria, and preparing initial timelines or budget frameworks.

Rather than replacing writing, this approach shifts staff effort from drafting repetitive content to reviewing, refining, and contextualizing proposals.

3. Centralizing Submission and Tracking Processes

AI-supported workflow tools consolidate deadlines, documentation requirements, and submission stages into a single system. This improves visibility across active applications, reduces reliance on manual tracking, and helps teams manage multiple proposals without last-minute bottlenecks.

Automated reminders and status updates support consistency, particularly when teams are working across overlapping funding cycles.

Human Oversight and AI as an Assistant

Human oversight guiding AI-supported grant workflows.

While AI reduces repetitive workload, decision-making and final review remain human-led. Staff continue to determine which opportunities to pursue, how projects are framed, and whether proposals align with organizational priorities and community needs.

AI supports tasks such as information retrieval, draft structuring, and formatting, but does not replace strategic judgment or ethical consideration. Proposals are reviewed and refined to ensure they reflect the organization’s voice, values, and long-term objectives.

This human-in-the-loop approach allows NGOs to benefit from efficiency gains without compromising authenticity, accountability, or mission alignment.

Safeguarding Sensitive Data

Grant workflows often involve sensitive organizational information and, in some cases, data related to beneficiaries or partners. Any use of AI in this context requires clear safeguards to ensure privacy, control, and responsible handling.

In practice, several measures are typically applied to reduce risk:

Data Anonymization

Personally identifiable or sensitive information is removed or masked before processing, limiting unnecessary exposure during automated analysis.

Encrypted Data Handling

Information is protected using standard encryption protocols both during transfer and while stored, reducing vulnerability across systems.

Compliance with Privacy Standards

Grant workflows are designed to align with applicable data protection regulations. Data is not retained or reused beyond its intended purpose without explicit authorization.

Local and Open-Source Deployment Options

In cases where data sensitivity is high, AI models can be deployed locally or through open-source frameworks such as GPT-NeoX or LLaMA. This allows organizations to maintain direct control over data flow and avoid dependence on external servers.

Together, these practices help ensure that efficiency gains from AI do not come at the expense of confidentiality, trust, or ethical responsibility—an essential consideration in the NGO sector.

Observed Outcomes

Changes to the grant workflow led to measurable improvements across several stages of the application process. By reducing manual effort and restructuring repetitive tasks, teams were able to work more consistently and with greater predictability.

Key outcomes included:

Shorter Proposal Turnaround

Proposals that previously required several weeks of preparation were completed in significantly less time, in some cases reducing turnaround by up to 50%. This allowed organizations to respond more quickly to funding opportunities.

Improved Consistency Across Applications

With clearer structure and better alignment to funder requirements, proposals became more consistent in format and content. This reduced rework and supported stronger positioning across submissions.

Reclaimed Staff Capacity

As administrative workload decreased, staff were able to shift attention back to program delivery, partner coordination, and longer-term planning rather than ongoing proposal management.

Taken together, these outcomes suggest that efficiency gains came not from working faster alone, but from reducing friction in the process—allowing grant work to support, rather than compete with, an organization’s core mission. Similar patterns are emerging across the sector, with many NGOs leveraging AI to improve efficiency, transparency, and scale across fundraising, operations, and program delivery.

Case Example: Improving Grant Application Efficiency

A nonprofit organization sought to improve the efficiency of its grant application process after repeated delays caused by limited staff capacity and fragmented workflows. Identifying relevant funding opportunities and completing proposals on time had become increasingly difficult alongside ongoing program responsibilities.

After restructuring its grant workflow with AI support, several changes became apparent:

• More than 20 relevant grant opportunities were identified within a single week
• Proposal drafting time was reduced by approximately 60%, saving over 100 staff hours
• Within six months, the organization secured approximately $500,000 in new funding

These changes allowed the team to shift attention back toward program implementation and community engagement while maintaining a more consistent and manageable funding pipeline.

Case Study 1: Streamlining NGO Grant Access with AI

NGO Grant Access with AI

The Challenge

Many NGOs relied on manual and time-intensive methods to identify grant opportunities. Limited capacity often resulted in delayed applications or missed funding altogether.

Omdena’s Approach

Working with the social enterprise Our Community, AI-supported tools were implemented to automate grant sourcing and analysis. This included document analysis, continuous data updates from multiple sources, and dashboards providing visibility into grant trends and eligibility across regions.

Impact

Grant data collection was largely automated, reducing research time and improving visibility into available funding. NGOs were able to apply more strategically, shifting effort away from administrative tasks toward program and community impact.

Case Study 2: Expanding RFP Access for Underrepresented Businesses

The Problem

Minority-owned, women-owned, and veteran-owned businesses faced persistent challenges in accessing government and commercial contracting opportunities. RFPs were scattered across multiple sources, making discovery time-intensive and limiting the ability of smaller businesses to compete effectively.

Omdena’s Approach

Omdena developed a centralized RFP platform that consolidated opportunities into a single searchable system. The implementation included automated data collection, structured data pipelines, and an interactive interface with advanced filtering, along with security and geo-fencing controls.

Impact

The platform significantly reduced time spent searching for opportunities and improved access to relevant contracts. As a result, underrepresented businesses were better positioned to participate in federal and commercial markets, supporting broader economic inclusion.

Addressing Barriers to AI Adoption

Despite potential efficiency gains, some NGOs remain cautious about adopting AI. Common concerns include budget constraints, limited technical capacity, and the need to protect sensitive data. In practice, adoption has been more feasible where these constraints are addressed at the workflow level rather than through large-scale technical change.

Three factors have proven particularly important.

Cost and Scalability

AI-supported grant workflows can be implemented using existing systems with limited additional infrastructure. This lowers upfront costs and allows organizations to scale gradually, based on need and capacity rather than fixed investment.

Limited Technical Requirements

Effective use of AI in grant processes does not require in-house data science teams. Implementation and maintenance are often handled through external collaboration, allowing NGO staff to focus on operational and strategic work rather than technical management.

Data Protection and Ethical Use

Adoption depends on clear safeguards around data handling, transparency, and accountability. AI-supported workflows are most successful when privacy considerations are built in from the outset and aligned with organizational values and sector expectations.

Taken together, these conditions help make AI adoption more practical and sustainable, particularly for organizations operating with limited resources.

Conclusion

This case illustrates how changes to grant workflows—rather than improvements in writing alone—can significantly reduce the time and effort required to pursue funding. By using AI to support grant discovery, early-stage drafting, and submission management, NGOs were able to shorten turnaround times while maintaining consistency and human oversight.

More importantly, reducing administrative friction allowed teams to redirect attention toward program delivery and long-term planning. When applied with clear boundaries and ethical safeguards, AI functions less as a replacement for human expertise and more as supporting infrastructure helping grant work fit more sustainably alongside an organization’s core mission