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Large Language Models' Potential for Grants Management: Opportunities and Challenges

The Large Language Models (LLMs) that power generative AI tools like OpenAIs ChatGPT are rapidly emerging as a transformative technology with the potential to revolutionize many industries, including grants management. While LLMs offer a wide range of applications across various domains, their true potential shines when applied to an organizations unstructured data. 


In the context of grants management, this translates into a wealth of possibilities. Here are a few examples:  

  • Answering questions about grant manuals and policies: LLMs can serve as virtual assistantsproviding accurate and context-aware responses to inquiries from grantees, subrecipients, and internal staffensuring consistent interpretation and understanding of complex grant requirements. 
  • Summarizing Notices of Funding Opportunities (NOFOs): LLMs can quickly distill the essence of lengthy NOFOs, allowing grant professionals to quickly assess eligibility criteria, application requirements, and funding priorities, thereby streamlining the proposal development process. 
  • Analyzing awarded vs. non-awarded applications: By ingesting historical application data, LLMs could be used to identify key success factors and patterns that differentiate successful proposals, thus providing valuable insights to improve future submissions. 
  • Tracking program success and risk through subrecipient reports: LLMs can continuously monitor subrecipient reportsflagging potential risks, identifying areas of concern, and highlighting successful initiativesenabling proactive risk management and program optimization. 


While the potential of LLMs in grants management is vast, there are several challenges that must be addressed. Data security and control are paramount concerns, especially when leveraging off-the-shelf LLM solutions that involve uploading potentially sensitive data to third-party servers. Model reliability and the potential for updates to break existing functionality are also issues that need to be carefully managed. The “hallucination problem” inherent to LLMs, where they can generate plausible-sounding but factually incorrect outputs, necessitates robust validation and quality control measures to ensure the accuracy and integrity of LLM-generated insights. 

One of the most significant challenges is perhaps structuring your unstructured data. Documents need to be organized and turned into a format that a machine can easily understand. Organizations using a document management system or grants management software that allows for the addition of custom metadata or tagging of documents and text segments have a head start with this. 


For organizations eager to harness the potential of LLMs in their grant programs but uncertain where to begin, an excellent starting point is exploring the features and limitations of readily available off-the-shelf solutions. This initial hands-on experience can provide valuable insights into the capabilities and nuances of LLMs. Concurrently, organizations should invest time in learning about the various components of the generative AI workflow and how model tuning can be used to ground responses to a source of truth, thereby minimizing inaccuracies and biases. Crucially, conducting comprehensive data audits and readiness assessments is a critical prerequisite to ensure that the data fueling AI initiatives is accurate, reliable, and poised to drive meaningful outcomes aligned with your organizations goals and priorities for grants management. 


As the capabilities of LLMs continue to evolve, their potential applications in the grants management domain are vast and exciting. By harnessing the power of these cutting-edge AI technologies, organizations can streamline processes, gain invaluable insights, and drive more effective and impactful grant programs. However, realizing this transformative potential requires a thoughtful and strategic approach that addresses key challenges around data security, model reliability, and factual accuracy. Future blog posts will delve deeper into these topics. Please feel free to reach out if youre interested in exploring solutions tailored to your organizations needs. 



Jason Mistlebauer
Director, Grants & Policy

Serving as Director, Grants & Policy for Witt O’Brien’s Community Services practice, Jason brings more than thirty years of professional experience in the public sector as well as non-profit and philanthropic organizations with expertise in program development, implementation, and management. Additionally, he provides Federal policy updates and analyses that may affect Witt O’Brien’s many clients. 

Matthew Atkinson

Matthew Atkinson
Vice President of Marketing

Matthew brings over a decade of experience in leveraging cutting-edge technologies to drive innovative strategies. With a focus on the transformative potential of generative AI, he has spearheaded the department’s adoption of generative AI tools to increase productivity. In addition, he leads a team of talented marketing and communications experts in delivering cutting-edge marketing strategies and tactics to both internal and external clients. 

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